literature review remote control

• Corpus ID: 246996251

REMOTE WORKING: A LITERATURE REVIEW

• Workplace Isolation , I. the , +2 authors Nguyen Thi Ngoc Ha
• Published 2022
• Sociology, Business

38 References

Workplace isolation occurring in remote workers, a review of causes and consequeces of workplace isolation, achieving effective remote working during the covid‐19 pandemic: a work design perspective, telework and organizational citizenship behaviors: the underexplored roles of social identity and professional isolation, workplace isolation: exploring the construct and its measurement, the job characteristics model: an extension to entrepreneurial motivation, social isolation and stress as predictors of productivity perception and remote work satisfaction during the covid-19 pandemic: the role of concern about the virus in a moderated double mediation.

• Highly Influential
• 10 Excerpts

Linking Transformational Leadership to Work Engagement and the Mediator Effect of Job Autonomy: A Study in a Turkish Private Non-Profit University

Social isolation and acceptance of the learning management system (lms) in the time of covid-19 pandemic: an expansion of the utaut model, challenges of teleworking during the covid-19 pandemic, related papers.

Showing 1 through 3 of 0 Related Papers

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Environ Res Public Health

Remote Working and Work Effectiveness: A Leader Perspective

Associated data.

All necessary data samples are provided in the paper.

Currently, job duties are massively transferred from in-person to remote working. Existing knowledge on remote working is mainly based on employees’ assessment. However, the manager’s perspective is crucial in organizations that turned into remote work for the first time facing sudden circumstances, i.e., SARS-CoV-2 pandemic. The main aim of our study was to analyze remote work effectiveness perceived by managers (N = 141) referring to three crucial aspects, i.e., manager, team, and external cooperation. We assumed the perceived benefits, limitations, and online working frequency as predictors of remote work effectiveness. Further, we analyzed the possible differences in remote work perception referring to different management levels (i.e., middle-level and lower-level). Our findings revealed a significant relationship between the benefits and effectiveness of managers and external cooperation, specifically among lower-level managers. Limitations, particularly technical and communication issues, predicted team and external cooperation effectiveness. The results showed remote work assessment as being socially diverse at the management level.

1. Introduction

Currently, remote work has become a crucial organizational tool that enables effective performance in the increasingly competitive global market. Although working outside of the office has already been available, this form of performing job duties seems mainstream in modern organizations. Due to the SARS-CoV-2 pandemic, 14.2% of employees in Poland changed their current way of performing professional duties to a remote mode. Almost every sixth employee in the public sector and every twelfth in the private sector worked remotely [ 1 ]. 85.6% worked remotely for five days a week, and 64% were likely to perform their professional duties remotely even after returning to the work office, especially since 44% of employees declared that their efficiency at home did not decrease [ 2 ]. Half of them indicated that sufficient work outside of the office was performed mainly for two days, and every seventh employee pointed out three remote working days.

Since the COVID-19 pandemic, many studies have been conducted on various aspects of remote working from the employees’ perspectives [ 3 , 4 , 5 , 6 , 7 ]. Generally, employees find working from home productive, albeit managers are often concerned about maintaining job performance at least on the same level as office work [ 8 , 9 ]. Thus, it seems crucial to look at how managers at different levels of management perceive the introduction of remote working on an unprecedented scale since they are responsible for organizing and controlling the employees’ work [ 10 ]. We decided to use managerial perception as previous research has proved the usefulness of subjective performance measures and their similarity with objective internal performance [ 11 , 12 , 13 ]. This study aimed to determine how managers rated the effectiveness of their own work and how they assessed the effectiveness of their team and external collaboration while performing their job duties remotely.

Literature Review and Hypotheses Development

Managers’ effectiveness has been defined as the impact of managers on the fluent functioning of an organization [ 14 ]. They can manage effective performance by using optimal acquisition and utilization of internal and external resources, i.e., human, financial, and instrumental resources. Since the managerial role is crucial in obtaining effective workflow and outcomes, this study was focused on managers’ perspectives.

Managers have different needs depending on their status [ 15 ]. Most often, the structure of managers in an organization consists of three levels [ 16 , 17 ]. The first one is top management which assumes top managers with most power, authority, and responsibility. The managers at this level define the company’s strategy, vision, and mission. They represent the company externally and visualize and define the company’s future. Top management is also responsible for dealing with the groups or individuals who may have different interests or intentions that do not have to align with the company’s interests. Their role is to unite or convince them that the interest of the organization stands above everything and is not in conflict with their actions [ 18 ]. The second level, namely middle management, is the one that sets the goals to achieve the organization’s strategy. Middle managers are tasked with communicating and implementing the plan received from top management [ 19 ]. They indicate organizational roles, and they work mainly with the low management. Thus, they rarely have contact with first-line workers. [ 20 ]. At the lowest level of the managerial hierarchy, lower-level managers usually have the most direct and frequent contact with front-line employees. As a result, low managers can significantly impact work effectiveness [ 21 ] since they operate and plan in the short term. They usually do not have the power to implement their own initiatives that can change the strategic goals [ 19 , 22 ]. Nevertheless, to ensure the stable functioning of the organization in unstable circumstances (e.g., at the time of the pandemic), they play a crucial role as first-line leaders. Therefore, the main objective of our study was the assessment of how managers with direct contact with subordinates (i.e., low- and middle-level managers) perceived work effectiveness.

The environment in which an organization finds itself is volatile, and managers at all levels should be open to change. Increased performance and job satisfaction from the perspective of individual employees are reported in trade journals [ 23 ] and academic sources [ 24 ]. However, the relationship between remote working and performance has not been well established from the managers’ perspective [ 11 , 12 , 13 , 25 , 26 ]. Virtual working, including working from home, comprises different benefits, e.g., saving time and other expenses, integrating the work of specialized employees, and expanding external co-operation. There is abundant research on the benefits and limitations of remote working [ 27 ]. The most common benefits include no commuting, reduced distraction, work–life balance and increased work flexibility, creativity, and motivation [ 28 , 29 ]. In addition, many studies have shown increased productivity [ 30 , 31 ]. Research indicates that proximity to co-workers often leads to wasted time and decreased productivity. The increased efficiency of employees in remote working is due to the lack of distractions present in the office [ 32 ]. On the other hand, employees indicate that the most significant disadvantage of remote work is the lack of non-work-related contacts [ 33 ], even though they can contact others via information and communication technologies (ICTs) [ 34 ]. Although Gibbs, Mengel, and Siemroth [ 27 ] emphasized that productivity depended on the worker’s characteristics, and measured employee productivity, the employees were able to maintain similar or slightly lower levels of output during work from home. Besides its positive aspects [ 30 , 35 ], existing research indicated a number of challenges generated by remote work, such as work–home interference, ineffective communication, procrastination, and loneliness.

As mentioned above, there are many advantages of remote forms of performing job duties, and several limitations that result in work outcomes and collaboration [ 31 ]. The responsibility of managing the remote work of employees rests with managers, particularly first-line managers and team leaders. Therefore, we assumed that the perceived effectiveness of remote work was connected with the experienced benefits and limitations ( cf. Hypothesis 1). Moreover, different management levels, i.e., middle- and lower-level managers, might perceive remote work differently ( cf. Hypothesis 2).

The perceived benefits, limitations, and frequency of remote work are related to the remote work effectiveness perceived by lower-level and middle-level managers.

The perceived remote working conditions differ between lower-level and middle-level managers.

2. Materials and Methods

2.1. participants and procedure.

To evaluate the effectiveness of remote work, we recruited employees from one of the largest enterprises in Poland. The companies that provided data belong to one of Poland’s largest capital groups in the energy sector. The survey covered the executive staff of three companies employing 234 middle- and lower-level managers (68 women and 166 men). A total of 29% were middle-level managers. The survey mainly addressed managers who had worked remotely/hybrid since the beginning of the COVID-19 pandemic. Two of the three companies surveyed previously could use remote working, but no more than two days per month. One company did not have remote working in operation. A vast majority of the managers were college-educated employees. As a result of the COVID-19 pandemic, all companies included in the survey had started remote working with the possibility of hybrid working. In the interests of employees, it was recommended that all individuals who were able to perform their duties (i.e., had the appropriate equipment) and agreed to work remotely took advantage of this opportunity.

We focused explicitly on the management staff during recruitment, i.e., department executives. Overall, the sample comprised 141 participants, including 18.7% middle management and 81.3% lower management. A total of 71% of participants were male, which reflects a male predominance in the real structure of the labor market and the share of males in the total number of employed managers in Poland [ 36 ]. All respondents were highly skilled and educated, mainly in the engineering field.

This cross-sectional study was based on anonymized employee data selected from the organizational resources. No person-related data were collected to ensure the anonymity of the study. The respondents received a link that directed them to the survey located on the company intranet. Participation was voluntary and free of charge. The participants were informed of the voluntary nature of participation in the study and the anonymity of data collection, i.e., their data would be analyzed collectively, and no personal information would be shared. They were assured that there were no wrong answers and that all of their opinions were important. Prior to participation, the respondents provided oral consent to participate in the study and were informed about the possibility of withdrawing from the study. All employees were aged 18 or older and completed their duties remotely from home.

2.2. Measures

Work effectiveness was assessed with three items related to different remote work effectiveness dimensions, i.e., the respondents were asked to assess the effectiveness of their own work, of the team, and of the co-operation with other business areas. All items required the participants to rate the extent to which they perceived work effectiveness (sample question: “Taking everything into consideration, how do you rate your work effectiveness as a whole?”) in all dimensions using a 5-point scale from 1 (ineffective) to 5 (very effective). Each dimension contained one-item measures. Using single-item measures is effective and more favorable in some respects than using multiple-item measures [ 37 ]; e.g., single-item measures are easier to understand by management, are completed more quickly, and require less effort. Higher scores indicated a higher level of perceived effectiveness in each dimension. The reliability of the scale comprising all three items in the current study was considered good, with Cronbach’s α = 0.8.

Benefits were measured using the one-item scale to assess perceived advantages of remote work with multiple-choice answers (sample categories: possibility to gain technical skills, on-task concentration, organized home life, and work economy). The list of chosen benefits was evaluated in terms of subjective fulfillment of criteria for remote working benefits by using competent judges. Benefits were defined as positive aspects, advantages, or profits gained from remote work. We asked five professionals, who were psychologists and managers, to evaluate the set of benefits on a 5-point scale (1 = does not refer to the dimension; 5 = fully refers to the dimension) and inspected the judges’ congruency concerning individual ratings (congruency index = 0.95). The ten benefits of remote work were positively verified by all five judges and were included in the study. The respondents reported the perceived benefits by checking them on a prepared list. The sum of selected benefits indicated the level of perceived benefits gained from remote work. In other words, a higher score indicated a larger number of benefits of remote work.

Limitations were measured with multiple-choice answers using a three-item scale assessing three dimensions of perceived disadvantages of remote work (i.e., organizational, technical, and social limitations). Limitations were defined as work aspects that limit the quality or achievement during remote work. The given limitations were verified by competent judges (congruency index = 0.93) and were introduced to the study. The overall-limitations measure was obtained by summing reported limitations from the possible ten statements which tap the various remote job facet (e.g., organizational, technical, and social issues). Higher scores indicated a higher level of limitations of remote work. The reliability of the scale comprising all three items in the current study was satisfying, Cronbach’s α = 0.7.

The respondents indicated the number of days of remote work per week to gain satisfactory team effectiveness, and the number of days of remote work per week to gain satisfactory management effectiveness. They rated on a scale between one to five working days.

Table 1 displays means, standard deviations, and correlations for the study variables.

Means ( M) , standard deviations ( SD ), and correlations between study variables.

Notes. Limit_org—limitations in the organizational dimension; Limit_tech—limitations in the technical dimension; Limit_soc—limitations in the social dimension; Online_leader—number of days of remote work to maintain high management effectiveness (per week); Online_team—number of days of remote work to maintain high team effectiveness (per week); Effect_leader—leader effectiveness; Effect_team—team effectiveness; Effect_co—external co-operation effectiveness; a Position is dummy-coded (1 = middle-level manager, 0 = lower-level manager); * p < 0.05; ** p < 0.01; *** p < 0.001.

The management position (i.e., lower-level and middle-level management) was negatively related to the perceived benefits ( p ≤ 0.05) and work effectiveness ( p ≤ 0.05), and positively associated with social limitations ( p ≤ 0.05).

In the first step, a regression analytical procedure was conducted to test the interaction between remote work conditions, i.e., benefits, limitations, online working frequency, and remote work effectiveness ( cf. , hypothesis 1). The regression model explained 37% of the variance in managers’ effectiveness (F(2, 134) = 17.94, p < 0.001), 31% of the variance in team effectiveness (F(2, 134) = 15.89, p < 0.001), and 37% of the variance in external co-operation efficacy (F(2, 134) = 13.45, p < 0.001). The managers’ position was dummy-coded and contrasted with “lower-level managers” and “middle-level managers”. The results are given in Table 2 .

Hierarchical linear regression of three aspects of remote work effectiveness.

Notes. Limit_org—limitations in the organizational dimension; Limit_tech—limitations in the technical dimension; Limit_soc—limitations in the social dimension; Online_leader—number of days of remote work to maintain high management effectiveness (per week); Online_team—number of days of remote work to maintain high team effectiveness (per week); a Position is dummy-coded (1 = middle-level manager, 0 = middle-level manager); * p < 0.05; ** p < 0.01; *** p < 0.001.

Table 2 shows the regression analysis of the relationship between dependent variables, i.e., manager effectiveness, team effectiveness, co-operation effectiveness, and predictors. Leader effectiveness was negatively related to a managerial position. The managers’ position was dummy-coded (0 = lower-level management; 1 = middle-level management). As shown in Table 2 , middle-level managers perceived the effectiveness of their work as lower ( β = −0.15, p < 0.05). Positive relationships were observed between the perceived benefits of remote work ( β = 0.14; p < 0.05), online working days ( β = 0.34; p < 0.01), and managers’ effectiveness. The same regression analyses were conducted for team effectiveness and relations with the external environment. Team effectiveness perceived by managers was negatively related to the experienced technological limits during remote working ( β = −0.20; p < 0.05) and positively related to the number of online working days ( β = 0.33; p < 0.05). The results showed that co-operation effectiveness was negatively related to the perceived technological limitations ( β = −0.18, p < 0.01), positively associated with the perceived benefits ( β = 0.22, p < 0.01), and positively associated with the frequency of remote work of managers ( β = 0.09, p < 0.05) and the team ( β = 0.32, p < 0.05).

Secondly, we assessed the significance of mean differences in remote work conditions perceived by lower-level and middle-level managers ( cf. hypothesis 2). The scores were normalized to a 0 to 1 range. We applied a Mann-Whitney U test that showed significant differences in the level of the perceived benefits of remote work between these groups (U = 642.50, p = 0.04). Middle-level managers perceived lower benefits ( M = 0.29) compared to lower-level managers ( M = 0.38). Analyzing the online work limitations, we found significant differences in the level of social limits (U = 1138, p = 0.02) and work effectiveness, (U = 519, p = 0.02) between the groups. Middle-level managers reported a higher level of social limits ( M = 0.30) compared to the lower-level managers ( M = 0.22). However, lower-level managers assumed themselves as more effective ( M = 4.37) compared to middle-level managers ( M = 3.95).

Based on the Mann-Whitney U test results, Figure 1 and Figure 2 present the benefits and limitations perceived by the analyzed groups in more detail. The p -value demonstrates significant means differences between the low- and middle-level management.

Remote work benefits perceived by lower- and middle-level managers. Notes. * p < 0.05; ** p < 0.01.

Remote work limitations, perceived by lower- and middle-level managers. Notes. * p < 0.05; ** p < 0.01; *** p < 0.001; + p < 0.10.

We further tested the relation between the specified benefits (i.e., on-task concentration), limitations (i.e., lack of rules, decreased work productivity, poor communication), and perceived work effectiveness that significantly differentiated managers on different management levels. A Mann-Whitney U test showed that the communication issue and perceived own work effectiveness revealed a differential pattern (U = 1993.50, p = 0.02). In other words, managers who reported poorer communication as a limitation of remote working had a lower level of the perceived own work effectiveness than those who indicated no communication issues. A significant difference was observed in work effectiveness referring to perceived productivity (U = 1882.50, p = 0.001). A lower level of managers’ effectiveness was shown in managers who experienced lower productivity.

Although the lack of rules did not significantly differentiate own work effectiveness, the perceived effectiveness of co-operation with the environment was significantly different for managers who “suffered” more from a lack of rules than those who did not complain (U = 1099, p = 0.03).

On-task concentration reported by managers was significant in differentiating their work effectiveness (U = 1475, p = 0.001) indicating that managers who reported on-task concentration as a remote work benefit perceived better work effectiveness.

4. Discussion

The COVID-19 virus outbreak has made many people work from home on an unprecedented scale, especially in business sectors where employees had not had an opportunity to work remotely before. Consequently, we argued the necessity of conducting research to confirm the effectiveness of remote work in this unique context, particularly from the managers’ perspective.

First, we examined the role of the perceived benefits, limitations, and online working frequency in maintaining high work effectiveness in three dimensions (i.e., manager, team, and external collaboration levels). Our findings showed benefits as significant predictors of perceived manager and co-operation effectiveness. The more benefits managers reported, the more effective they felt at work. Therefore, activating the available strengths of remote work empowers organizational resources and work effectiveness. Available communication devices allow quicker performance of the tasks e.g., organizing and attending work meetings online is faster and easier compared to organizing face-to-face contacts [ 38 ]. This relationship mainly concerns lower-level managers. From the managers’ perspective, the benefits were not as important in predicting the team’s effectiveness. The results indicated significant relationships between technical limitations and effective remote work in team and external collaboration. Technical issues were perceived as lowering work effectiveness, independently of the manager’s management level (i.e., middle-level and lower-level).

Further analysis demonstrated the differences in the perception of work effectiveness among managers at different levels of management (i.e., lower-level and middle-level management). In the context of remote working introduced on such a large scale during the COVID-19 pandemic, our findings highlight that, on the one hand, increased effectiveness and perceived benefits can be observed. On the other hand, they are not at the same level depending on the management role connected with social interactions.

Our findings offer managers a new lens to view the advantages/disadvantages of working from home. Generally, employees’ lack of social interactions is perceived as a disadvantage [ 34 ]. Nevertheless, this study proposes an alternative view of telecommuting that can boost performance as a result of improving technical support and minimalizing unnecessary distractions. Although, Allen, Golden, and Shockley [ 9 ] emphasized that social relationships at work can suffer as a result of excessive remote work, and care should be taken to properly manage the negative effects of weakened relationships between employees. We cannot lead to workplace loneliness which can result in lower job performance [ 39 ] as a result of informal interactions and a team cohesion decrease [ 7 ]. The results showed that the possibility of concentration on the task was evaluated higher by lower-level managers. Work that requires more on-task concentration and problem-solving is done more preferably at home, with significantly fewer distractions [ 29 , 40 ]. As mentioned before, lower-level managers have more frequent contact with employees than higher-level managers, and recent research suggests that calls between remote workers are more task-focused and less distracted [ 32 , 34 ]. Consequently, referring to perceived remote work limitations, organizational issues (e.g., lack of rules), and social issues (i.e., lower productivity and ineffective communication with employees) significantly differentiated the managers at different managerial levels. The middle-level managers suffered more from the specific remote work limitations.

By identifying differences in the managerial levels in the perceived benefits and limitations, our findings shed light on a specific explanation as to why remote working is perceived more favorably by lower-level managers. Therefore, our empirical studies on how social implications of remote working can affect work effectiveness [ 32 ] indicated that a lack of distractions can increase workers’ effectiveness while working from home. We do not argue that the effectiveness of the remote mode is only due to employees’ lack of distraction in the home office. The perceived benefits and technological issues are also related to work effectiveness. An understanding of how managers perceive remote work and its effectiveness at different managerial levels and the discrepancy in the perception of benefits and limitations is crucial for understanding remote work effectiveness, especially since remote working offers indisputable convenience, which will contribute to its expansiveness in the organizational setting compared to the pre-COVID-19 level.

4.1. Limitations and Direction for Further Research

Despite the contributions we make, this study is not without limitations. First, our research did not explore the employees’ perspective or objective internal performance or work characteristics. Nonetheless, the managerial perspective is relatively rarely analyzed. Future research could explore how employee attributes and other factors such as personality or stress may shape the effectiveness of working online. Second, the sample size was comparatively small, with a male predominance, which limits the generalizability of the findings and the opportunity to explore other moderating mechanisms. Nevertheless, the sample provided sufficient statistical power to test the hypothesized relations. Next, our study was designed as cross-sectional. Considering the specificity of the sample and contextual conditions (i.e., pandemic), the cross-sectional design seemed reasonable and indicated the most significant relations. Finally, we used self-reported measures that are often the only possible way to examine one’s own perspective, such as self-perceived effectiveness in a specific context [ 34 ]. Nonetheless, there is still the need to use objective methods and include the employees’ perspective in the study. Using objective information (e.g., Key Performance Indicators or Return on Investment) could help solve this potential bias in the data in a future study.

Remote working in Poland is relatively new and introducing it on a such significant scale might provide unique experiences. Little is known about both direct and ripple effects that can bring us a widespread shift to remote work. Additionally, it would be useful to analyze the further relationship between social interactions and effectiveness by using objective measures. Further research requires more information concerning working online from a leader’s perspective. Longitudinal research would be necessary to demonstrate the development and changes of home office effects. Although the consideration of a leader’s perspective has given us new insights, avoiding a biased managerial perception of remote working as less effective is helpful. A more specific analysis of job characteristics and effectiveness can reveal conditions that are advantageous for employers and employees. Further interaction effects between remote work and HRM policies, as well as between social interactions, should be studied.

This study was conducted during the COVID-19 pandemic for the first time. In order to rule out the impact of pandemic stress and its effect on effectiveness, it is necessary to repeat the study after the epidemiological threat has ceased. If home-office information on a management level is available, and if a comparison during and after the coronavirus crisis is possible, we can learn whether COVID-19 has contributed to a substantial structural change.

Other constraints that can affect leaders and managers are those that also can be connected with the issues that are familiar from the perspective of employees. One such constraint, for instance, might be the low turnover and the intensity of hiring, which was limited. In the case of employees, a decline in efficiency can be observed, which could be partly traced to having less experience, lower tenure, or being in the process of onboarding [ 27 ].

4.2. Practical Implications

This study provides meaningful implications for practitioners. First, our research suggests that effectiveness can be increased by managing remote work effectively and implementing HR policies to strengthen the benefits of remote work and minimalize shortcomings, mainly in technical dimensions (e.g., poor quality of internet connections, multiple communication channels), while organizations can set hybrid working from home and observe changes in the managerial perception. However, organizations may influence the supportive practices that come to managers of all levels. Employers can offer training on improving their managing skills in remote environments. Some researchers suggest that consideration should be given to the individual adjustment of work conditions (e.g., less disciplined employees might experience more challenges during remote working). Therefore, offering them online work would be unsuccessful [ 34 ].

Researchers emphasize the great role of managers and leaders in practicing working from home. They are ought to provide adequate support in response to the needs of employees with different challenges [ 7 , 34 ]. Otherwise, remote working might turn out to be ineffective causing problems such as a longer time spent on projects, difficulties with training, onboarding issues, etc. We can observe that, from a management point of view, working from home reached the highest level of productivity in COVID-19 and stabilized, but this situation might not be sustainable [ 40 ].

The main concern, from a managerial perspective, often suggested about working from home is a decrease in effectiveness [ 8 ]. Thus, it can have a negative effect on how they operate at different levels of management. This study contributes to clarifying this issue and gaining a better understanding of the sources of perceived effectiveness from the perspective of managers and leaders. It can have a positive impact on the level of employees’ commitment and dedication to their companies, resulting in higher effectiveness [ 8 ].

Without a doubt, remote work has become an inherent work system, and the challenge today is to maintain or indicate maximum efficiency. Undoubtedly, the best solution is to introduce hybrid work and combine remote work with office work [ 23 ]. It is necessary to take a closer look at the characteristics of the job in question and put in place solutions to perform tasks at their best, depending on whether it is more efficient to do them at home or in the office. So far, we know that some work is done effectively at home, while other work is better done at the office.

5. Conclusions

This study contributes to understanding how remote working influences effectiveness from the managers’ perspective. While previous research has recognized that working online may be more effective, the role of managers has received less attention, both theoretically and empirically. Generally, managers view remote working as resulting in decreased performance and lower managerial control [ 8 ]. Our study suggests that the more benefits managers perceive, the more effective their work is assessed in different dimensions (i.e., manager, team, external co-operation). Moreover, the results indicated the difference in remote work perception depending on the management level (i.e., lower-level and middle-level management). Managers who have more contact with employees are more aware of the benefits of working remotely. Accordingly, the perceived benefits are related to a higher level of reported work effectiveness.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, G.K. and K.Ś.; Formal analysis, K.Ś.; Investigation, G.K.; Methodology, G.K. and K.Ś.; Project administration, G.K.; Resources, G.K. and K.Ś.; Software, G.K.; Supervision, K.Ś.; Visualization, G.K. and K.Ś.; Writing–original draft, G.K. and K.Ś.; Writing–review and editing, K.Ś. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The current study was approved by the Research Ethics Committee, decision no. KEUS.67/11.2020.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

CHAPTER 2 LITERATURE REVIEW AND RESEARCH OBJECTIVES

Related Papers

Sumalatha Bandari

TJPRC Publication

Revolution in the technology helps to increase the living standards of human life and building automation technology has a significant contribution to this advancements. The concept of internet of things binds the object around, to the web world to facilitate the remote access and control. This paper presents sharing of real time data over the internet and enable interaction and control of the device from remote location. A stand alone web server is developed using LM3S8962 stellaris family microcontroller with inbuilt ethernet controller and Light weight Internet Protocol (LwIP) stack is ported, to facilitate the supervision of home appliance. A Graphical User Interface (GUI) developed using web technologies provide an effective user interaction. The functionality of the prototype system is checked and verified.

Valipi Mallikarjuna

… Conference, 2004. 5th …

Eka Gunawan

Muhamad Ismanto

Web Controlled Home Automation System is a system that can be used to control home appliances such as fan, air conditioner, heater, light, etc. through the internet. This system is used in this project to create a smart home. In the smart home system, users can activate home appliances wherever and whenever they are by using internet on PC's, Laptops, Smartphones, Tablets, and other internet connected devices. There are three (3) main functional blocks in this system: (i) webpage to provide Graphical User Interface (GUI), (ii) Controller Bridge consists of Arduino UNO and Arduino Ethernet Shield with, and (iii) Switching unit as a task executor. This project focus on building a simple system to control home appliances using user-friendly website page. There is also a built in system error detection. This project has two (2) main tasks. They are (i) Creating simple system for web controlled home automation system with a system error feedback reporting, (ii) Developing the system programming and creating user-friendly website page with interesting GUI. The main controller used an Arduino UNO board that is easy to program using Arduino IDE software [2] and able to control many devices. The system is interfaced to the internet through the Arduino Ethernet Shield using RJ45 cable. This Ethernet Shield is used to collect information from users and send it to the main controller and supports up to four simultaneous socket connection [1] .

Tarun Agarwal

With the growing popularity of Internet, Embedded Technology and Web Technology developing a fault tolerance system based on embedded web server, by using a Ethernet as communication media, this is finding wide spread application in embedded field. The proposed work plans to control the appliances placed in industrial area through the web server, in this plans to use of LPC1768 CORTEX-M3 based embedded board in the implementation of a Tiny web server (embedded web server) for control of industrial appliances in the server side. To communicate server with client an Ethernet is using here, Ethernet network communication Interface by using TCP/IP protocol and an Ethernet interface with HTML web page. This TCP/IP protocol is act as bridge between client and server and initialize to communicate. The webpage and firmware is done in HTML and dynamic C programming language respectively. Here the embedded system board acts as central heart of the server between webpage and appliances.

Marcin Jachimski , Pawel Kwasnowski

The paper presents a comprehensive description of a SMALL INTERNET MONITORING AND CONTROL DEVICE, named as: INTERNET LOGGER eLOGTM, designed and manufactured by the Experimental Department of Scientific Equipment and Automation Ltd in Cracow. It is a miniature, microcomputer remote data concentrator, using real-time operating system, designed for direct connection to the Internet/Intranet network. Visualization of acquired signals, as well as configuration and programming of the device are performed by means of www browser (using Java platform). For this purpose it was provided with the Ethernet 10BaseT interface, TCP/IP protocol and a www server (www@eLOGTM). Additional services installed are: Telnet, FTP, e-mail (e-mail@eLOGTM), PPP and sending short text messages to mobile phones (SMS@eLOGTM). The internet concentrator is designed for operation in distributed systems of data acquisition and recording. It provides an input/output unit for analog and binary signals. Also a method, ...

Editor IJRET

Adira Meempat

Pawan Kumar

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

IJESRT Journal

Proceedings of the Tenth International Conference on Enterprise Information Systems

jose Rodriguez

Journal of Network and Computer Applications

Reha Civanlar

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

A review on drones controlled in real-time

• Open access
• Published: 05 January 2021
• Volume 9 , pages 1832–1846, ( 2021 )

Cite this article

You have full access to this open access article

• Vemema Kangunde   ORCID: orcid.org/0000-0001-7169-7632 1 ,
• Rodrigo S. Jamisola Jr. 1 &
• Emmanuel K. Theophilus 1  

24k Accesses

47 Citations

Explore all metrics

This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

Similar content being viewed by others

Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends

Recent Advances in Unmanned Aerial Vehicles: A Review

Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review

Avoid common mistakes on your manuscript.

1 Introduction

A drone, also known as unmanned aerial vehicle (UAV), is an aircraft without a human pilot on board [ 1 , 2 ]. There has been a rapid development of drones for the past few decades due to the advancement of components such as micro electro-mechanical systems (MEMS) sensors, microprocessors, high energy lithium polymer (LiPo) batteries, as well as more efficient and compact actuators [ 3 , 4 , 5 ]. Drones are now present in many daily life activities [ 2 , 6 , 7 , 8 ]. They are used in many applications such as inspecting pipelines and power lines, surveillance and mapping, military combat, agriculture, delivery of medicines in remote areas, aerial mapping, and many others [ 2 , 9 , 10 , 11 , 12 ]. See Figs.  1 and 2 for some drones applications. Robotic manipulators, found in many applications [ 13 , 14 , 15 ], have in recent years been implemented on UAV platforms [ 16 , 17 , 18 ] for tasks such as aerial manipulation, grasping, and cooperative transportation. The unstable dynamics of the robotic arm, which increase control complexity of UAVs, have widely been studied in the literature [ 19 , 20 , 21 , 22 ].

UAVs technology is rapidly growing while UAV solutions are being proposed at faster rates as various needs arise. Drone features are determined by specific UAV applications as well as competition in the commercial market [ 23 , 23 , 24 , 25 ]. In [ 26 ], a review of the most recent applications of UAVs in the cryosphere was conducted. Compared to conventional spaceborne or airborne remote sensing platforms [ 27 , 28 , 29 ], UAVs offer more advantages in terms of data acquisition windows, revisits, sensor types, viewing angles, flying altitudes, and overlap dimensions [ 26 , 30 , 31 , 32 ]. The review shows that across the world, applications used various multirotor and fixed-wing UAV platforms. Red, green, blue (RGB) sensors were the most used, and applications utilised quality video transmission to the ground control station. The study in [ 33 ] demonstrates how versatile and fast-growing is the adoption of UAV solutions in daily life scenarios. They propose the design of a system capable of detecting coronavirus automatically from the thermal image quickly and with less human interactions using IoT-based drone technology. The UAV system is equipped with two cameras: an optical camera and a thermal camera. It conveys to the ground control station (GCS) the image of the person, the global positioning system (GPS) location as well as a thermal image of the hot body detected. The system combines IoT, virtual reality, and live video feedback to control the camera for monitoring people.

Picture reprinted from https://aibirduav.diytrade.com

The KC2800 is a fixed-wing drone used for surveillance and mapping

Picture reprinted from https://www.indiamart.com

Quadrotor drone spraying pesticide on crops

Picture reprinted from  https://thewiredshopper.com

Picture reprinted from https://thewiredshopper.com

On the other hand, apart from advancements in custom-made drones, commercial drone manufacturers are actively improving their products. Latest, more advanced drones are presented at https://thewiredshopper.com , see Figs. 3 and 4 . DJI Phantom 4, for example, is equipped with an automatic collision avoidance system. It has a sport mode that disables collision detection and enables fast speeds. It also has an active tracking technology that enables the selection of another moving object, like a car or another drone, and the Phantom 4 will autonomously follow it without assistance from the human pilot. The drone is equipped with a 3-axis camera and can record 4K resolution video at 30 fps and 1080p resolution at 12 fps. It will take 12-megapixel images in Adobe DNG raw format. It has gimbal stabilization technology and a built-in video editor. Other latest drones in the market include the AirDog drone by AirDog, 3DR Solo Drone by 3DRobotics, and Yuneec Typhoon H by Yuneec. A UAV’s operational environment is highly dynamic due to unpredictable changes in weather conditions affecting the air space. For drones to be reliable, their flight controllers must adapt to these environmental changes in real-time. Control of highly maneuverable UAVs has been extensively studied for the past decades.

2 Drone hardware overview

A UAV is controlled by an embedded computer called the Flight Control System (FCS) or flight controller [ 34 , 35 , 36 ], basically consisting of a control software loaded into a microcontroller. The microcontroller reads information from on-board sensors, such as accelerometers, gyroscopes, magnetometers, pressure sensors, GPS, etc.,as well as input from the pilot, perform control calculations, and control the motors on the UAV [ 37 , 38 ]. The FCS as well as the set of sensors would be mounted on the drone air frame. Drone air frames, typically made of strong, light composite materials, are mostly relatively small with limited space for avionics [ 39 , 40 ]. A set of sensors, such as TV cameras, infrared cameras, thermal sensors, chemical, biological sensors, meteorological sensors etc., used to gather information during drone applications need to be lightweight to reduce UAV payload [ 41 , 42 , 43 , 44 ]. The information gathered from the sensors can be partially processed on-board or transmitted to the ground station for further processing [ 45 , 46 , 47 ]. An on-board controller, separate from the flight controller, can be used to operate the payload sensors [ 48 , 49 , 50 ]. Figure 5 shows the Cc3d open source flight controller used as a UAV flight controller.

The Pixhawk flight controller is an open-source hardware project equipped with sensors necessary for flight control [ 51 , 52 , 53 ]. It includes a CPU with RAM as well as gyroscope, compass, 3-axis accelerometer, barometric pressure, and magnetometer [ 54 , 55 ]. The Paparazzi flight controller, developed by Ecole Nationale de lAviation Civil (ENAC) UAV Lab since 2003 [ 34 ], is the first and oldest open-source drone hardware and software project. In March 2017 ENAC Lab released the Paparazzi Chimera autopilot. A detailed survey on open-source flight controllers was disclosed by Ebeid et.al in [ 34 ]. An autopilot software is used for drone automatic flight control [ 56 ]. On the other hand, drones can be operated remotely through a remote controller [ 57 , 58 , 59 ].

Picture reprinted from https://www.google.com/search?q=Cc3d++flight+controller

UAV hardware components

2.1 State observation

The FCS requires information on UAV states such as attitude, position, and velocity for control implementation [ 60 ]. The commonly used state observer is the inertial guidance system. Other attitude determination devices such as infrared or vision based sensors can be used [ 61 , 62 ]. The inertial guidance system (IGS), also referred to as inertial navigation system (INS) [ 63 ] consists of the inertial measurement unit (IMU) and the navigation computer. The IMU has three orthogonal rate-gyroscopes, three orthogonal accelerometers and sometimes 3-axis magnetometer to determine angular velocity, linear acceleration and orientation respectively [ 64 ]. Inertial guidance systems are entirely self reliant within a vehicle where they are used. They do not rely on transmission of signals from the vehicle or reception of signals from external sources. Inertial guidance systems can be used to estimate the location of the UAV relative to its initial position using a method known as dead reckoning [ 65 ]. Global navigation satellite system (GNSS) provides location estimates using at least four satellites [ 65 ].

2.2 State estimation

State estimation feedback is required for UAV control, such estimates are usually for attitude, position, and velocity [ 66 ]. On board sensor readings are fed to the UAV autopilot system to generate UAV state estimates [ 67 ]. The need for state estimation is due to the fact that data from measurement sensors is prone to uncertainties due to atmospheric disturbances, vibrations noise, inaccuracy of coordinate transformations, and missing measurements [ 68 ]. Sensors such as the GPS suffers from signal obstruction and reflections caused by nearby objects leading to missing or inadequate information [ 69 ].

To compensate for uncertainties and lack of information from individual sensors, multiple sensor data fusion can be employed to incorporate advantages of different types of sensors [ 70 ]. The altitude heading and reference system combines gyroscope, accelerometer, magnetometer, GPS and pressure sensors to measure UAV states. Sensor data for state estimates need to be updated at a relatively high frequency, normally above 20 Hz for small UAVs. Kalman filtering can be employed to make optimal estimations for sensors with lower update frequencies, such as the GPS, which typically has an update frequency of 4 Hz. Kalman filtering can also be used to process gyroscope readings which are susceptible to noise and drift. The other technique to improve gyroscopic readings is to model the gyroscope random noise and then offsetting it according to the model, this is referred to as model compensation [ 71 ].

2.3 Controller design for autopilots

Most current commercial and research autopilots focus on GPS-based waypoints navigation to follow a desires path [ 72 ]. Waypoint navigation is essential for autonomous control of UAVs for UAV tasks beyond the pilot’s sight. The pilot could control the UAV from the GCS using a graphical User Interface (GUI), the location as well as other needed information about the UAV would be displayed at the the GCS [ 45 ]. The path following control of a UAV involves the control of roll, pitch, altitude and air speed for trajectory tracking and waypoint navigation [ 73 ]. GPS waypoint navigation involves providing sequential GPS coordinates that contains locations and heights of the UAV flight [ 72 ]. The set of pr-programmed GPS waypoints then becomes the path for the UAV to follow [ 74 ]. In

2.4 Microcontrollers used

An FCS has sensor packages for state determination, on-board processors for control and estimation uses, and peripherals for communication links and data transfer. For small UAV applications , small, light weight, and often low power consumption hardware components for the FCS are preferable. Successful UAV control requires sensors used for attitude estimation to have good performance especially in mobile and temperature-varying environments [ 75 ]. Arduino is an open-source electronics platform found in a wide variety of application projects. The board is capable of reading inputs from various sensors and generates required outputs. It comes comes with different processors and board sizes. Arduino Nano was used in [ 76 ] to develop an instrumentation system to collect flight data such as airspeed, orientation, and altitude, e.t.c. The system will then transmit the flight data over a radio frequency module.

2.5 Rotors configuration

There are different types of drones, they can generally be categorised as single rotor helicopter, fixed wing and multi-rotor drones [ 77 , 78 ]. Nowadays researchers endeavors to combine the advantages of fixed wing and multi-rotor drones [ 77 ]. Fixed wing drones are renowned for their endurance whereas helicopters and multirotors have the the advantage of VTOL as well as hovering. Quad-rotor drones are most common and belongs to the multi-copter family [ 77 ]. The quad-rotor unmanned aerial vehicle (UAV) are drones with four rotors typically designed in a cross configuration with two pairs of opposite rotors rotating clockwise and the other rotor pair rotating counter-clockwise to balance the torque. The roll, pitch, yaw and up-thrust actions are controlled by changing the thrusts of the rotors using pulse width modulation (PWM) to give the desired output [ 79 ]. Typically, the structure of a quad-rotor is simple enough, which comprises four rotors attached at the ends of arms under a symmetric frame. The dominating forces and moments acting on the quadrotor are given by rotors, driven with motors, mostly brushless DC motors. There are two basic types of quad-rotor configurations; plus and cross configurations [ 80 ]. The difference between these configurations is where the front of the quadcopter is located. To counteract reactional torque due to propeller rotation, two diagonal pair of motors (1 and 2) rotate anticlockwise while the other pair, motors (3 and 4), rotate clockwise [ 80 ]. In contrast to the plus configuration, for the same desired motion, the cross-style provides higher momentum which can increase the maneuverability performances, each move requires all four blades to vary their rotation speed [ 81 ]. However, the attitude control is basically analogous. Figure  6 shows the quadrotor cross and plus configurations respectively. The red cross depicts direction to the front of the quadrotor, in this case to the right of the pictures in the figure.

Picture reprinted from [ 82 ]

Quadrotor cross and plus Configuaration

The quad-rotors translational motion depends on the tilting of rotor craft platform towards the desired orientation. Hence, it should be noted that the translational and rotational motion are tightly coupled because the change of rotating speed of one rotor causes motion in three degrees of freedom. This is the reason that allows the quad-rotor with six degrees of freedom (DOF) to be controlled by four rotors; therefore the quad-rotor is an under actuated system [ 83 ]. In principle, a quad-rotor is dynamically unstable and therefore proper control is necessary to make it stable. Despite the unstable dynamics, it has good agility. The instability comes from the changing rotor craft parameters and the environmental disturbances such as wind. In addition, the lack of damping and the cross-coupling between degrees of freedom make it very sensitive to disturbances.

2.6 Sensors used

Essential to drone flight is the Inertial Guidance System, this is an electronic system that continuously monitors position, velocity and acceleration by means of incorporated sensor set. It consists of 3-axis rate gyro and 3-axis accelerometer as well as a magnetometer. The IGS readings are filtered to estimate the attitude of the UAV. Recent developments in computing and MEMs technology has seen the decrease in size of IGS sensors [ 84 ]. Thus for small UAVs, a micro IGS can be used to provide a complete set of sensor readings [ 75 ]. Attitude information can also be estimated using infrared (IR) thermopile sensors. They work on the fact that the earth emits more IR than the sky by measuring the heat difference between two sensors on one axis to determine the angle of the UAV. Other sensors such as Vision sensors, either by themselves or combined with inertial measurements sensors can also be used for attitude estimation [ 85 ].

3 Required software components for real-time implementation

Real-time control requires hardware and software systems to be implemented together. Several definitions for real-time systems can be found in the literature. A good definition that we found states that; “a real-time system is one in which the correctness of a result not only depends on the logical correctness of a calculation but also upon the time at which the result is made available” https://www.ibm.com . There is a time requirement, referred to as a deadline, under which the system tasks must be performed. The primary objective is to ensure a timely and deterministic response to events. In the context of drone control, such tasks are normally intended to react to external events in real-time. Thus such real-time tasks are required to keep up with external changes affecting drone performance. Tasks required to meet their deadlines to avoid catastrophic consequences are called hard real-time tasks. When meeting the deadline is desirable but not mandatory, the task is considered soft real-time task [ 86 ].

3.1 Real-time operating systems

A real-time operating system (RTOS) provides services such as multitasking, scheduling, inter-task communication, etc., to facilitate the implementation of real time-time systems [ 87 ]. An RTOS is the key component needed to build a real-time system. Other software pieces such as compilers, linker, debugger and drivers are necessary to interface with system hardware: https://www.ni.com . RTOSs are employed in the development of many applications such as Internet of Things (IoT), automotive , medical suystems, robotics, industrial automation, avionics, and flight control systems [ 88 , 89 ]. RTOSs mainly focus on task predictability and efficiency, therefore have features to support timing constraints for application tasks [ 90 ]. There are several categories of RTOS; small, proprietary kernels as well as real-time extensions to commercial time-sharing operating systems such as Unix and Linux. The kernel is the core, an essential center of the RTOS, or any computer operating system. It is responsible for memory management, processing, and task management, and to interface with hardware and application software. Small, proprietary kernels are often used in embedded applications when very fast and highly predictable execution must be guaranteed. Meeting time constraints requires kernels to be small in size, which reduces RTOS overhead. Kernels must also have a fast context switch, support for multi-tasking, priority-based preemption, provide a bounded execution time for most primitives, and maintain a high-resolution real-time clock [ 90 ].

3.2 Scheduling and prioritisation

Appropriate task scheduling in real-time applications is the basic mechanism adopted by an RTOS to meet time constraints of tasks [ 90 ]. It is the responsibility of the application developer to choose an RTOS that will schedule and execute these tasks to meet their constraints. For a given application, if a set of tasks can be scheduled such that they all meet their deadline, then the tasks are said to schedulable [ 91 ] In priority-driven (PD) scheduling, priorities are assigned to tasks. A task with the closest deadline than any other task is considered the highest priority task [ 92 ]. Embedded time critical applications employ the real-time scheduler to ensure low latency and meeting time constraints. Numeric priorities are assigned to threads constituting tasks, and only the highest priority task is selected to run by the scheduler. A higher priority task can preempt a lower priority task at any point of its execution [ 93 ].

However task priorities can also be dynamic such that a low priority task may temporary elevates its priority to prevent interruption during execution of its critical section. Preemption thresholds can also be set by considering task priority as well as task urgency. Both priority and Urgency are quantified such that it is possible for urgency to take precedence when scheduling tasks [ 86 , 93 ]. Multithreaded parallel programming systems (MPPS) has a characteristic that data is shared among threads. It is important that access to shared data is controlled to avoid associated concurrency errors. As an example, suppose a task alters or updates a global variable, it is necessary for the task to have exclusive access to that variable while it is executing, otherwise concurrent access to the same variable by other tasks will lead to data races, leading to miscompilations.Access of shared data by one task at a time can be achieved by use of Mutual exclusion locks (mutexes) [ 93 ].

3.3 Sensor inputs and feedback control

The common drone platform has a specialised software running on a computer at the ground control station. It allows users to monitor and send control messages to affect drone’s state and actions remotely. Aboard the drone, the autopilot software combines operator inputs and sensor feedback information to directly control UAV actuators [ 94 ]. Sensors onboard the UAV provide feedback data essential to determine the drone’s position and attitude. A stereo camera was proposed for obstacle avoidance as well as velocity estimation in [ 95 ]. In [ 96 ], vision and IMU sensors were employed for automatic navigation and landing of an AR drone quadrotor. A landing marker was positioned in the drone frontal camera’s sight of view, see Fig.  7 . The landing marker position is the desired position \(X_d\) = ( \(x_{d_G}\) , \(y_{d_G}\) , \(z_{d_G}\) ), which corresponds to a height above the landing marker. Position \(X = (x_G, y_G, z_G)\) denotes the drone current location. The position error is then denoted as \(E = X_d - X\) , where  \(E = (e_x,e_y,e_z)\) . The symbols \(e_x,e_y,e_z\) are position errors in directions \(X_G\) , \(Y_G\) , and \(Z_G\) , respectively. The PID controller was applied to the position error in accordance with ( 1 ) and ( 2 ). The drone will land when above the marker, i.e., when the error  \(E =0\) .

Picture reprinted from [ 96 ]

Automatic navigation and landing of an AR drone quadrotor

3.3.1 Localisation using differential global positioning system (DGPS)

Differential global positioning system (DGPS) is extensively used for accurate localisation of drones. The scope of localization and mapping for an agent is the method to locate itself locally, estimate its state, and build a 3D model of its surroundings, by employing among others vision sensors [ 97 ]. Towards this direction, a visual pose-estimation system from multiple cameras on-board a UAV, known as multi-camera parallel tracking and mapping (PTAM), has been presented in [ 98 ]. This solution was based on the monocular PTAM and was able to integrate concepts from the field of multi-camera ego-motion estimation. Additionally, in this work, a novel extrinsic parameter calibration method for the non-overlapping field of view cameras has been proposed.

3.3.2 Mobile phone technology in UAV applications

UAV applications encompass many areas, including, aerial surveillance ,reconnaissance, underground mine rescue operations, and so on [ 25 , 99 ]. Some of these application areas are GPS denied, thus GPS can not provide the location for a UAV. Currently, vision sensors, laser scanners, and the IMU are the most common position sensors used for UAV self-localisation. In some applications, small UAVs are preferred for their cost and high maneuverability. Considering the limited load capacity and the cost of small UAVs, it cannot be equipped with sensors of high precision and large volume [ 100 ].

Micro-electro-mechanical system (MEMS) sensors are therefore preferred alternatives because they are small and cheap. On the other hand, mobile phones contain multi-sensors, multi-core processors, have a small volume, and lightweight. In [ 101 ], Nexus 4 smartphone developed by Google, was used as a flight controller. The phone is equipped with inbuilt MEMS sensors such as accelerometer, gyroscope, magnetometer, global navigation satellite system (GNSS), and barometer. The implementation exclusively used sensors and processors from the smartphone, see Figs.  8 and  9 . Mobile phone usage possibilities in UAV platforms are further elaborated in [ 102 ], where a smart phone is proposed for implementation of drone control algorithms. The usage of smart phones can reduce development time as it it cuts down the need for integration of different drone hardware components, instead the proposed solution uses smart phone inbuilt sensors [ 102 ].

Picture reprinted from [ 101 ]

Schematic diagram for on-board smartphone flight controller using Arduino Mega to interface with the electronic speed controllers (ESCs)

Quadcopter used in [ 101 ] with an on-board smartphone as flight controller

3.3.3 Communication to the ground control station

Communication to the ground control station allows drone pilots to remotely configure mission parameters, such as coordinates to cover during way-point navigation and the action to take at each way-point. Most existing drone platforms have the configuration shown in Fig.  10 . A specialized software runs at a ground-control station (GCS) to let users configure mission parameters. The Ground Control Station is a system made up of software and hardware necessary for UAV remote control. Hardware, such as the joystic, takes the pilot’s command which is transmitted to the drone via radio transmitter. The GCS software collects tellemetry data transmitted from the UAV and displays it the on the GCS user interface [ 103 ]. Communication networking is responsible for the information flow between GCS and UAV on a mission. It needs to be robust against uncertainties in the environment and quickly adapt to changes in the network topology. Communication is not only needed for disseminating observations, tasks, and control information but also needed to coordinate the vehicles more effectively toward a global goal. The goal could be tasks such as areal monitoring or detecting events within the shortest time, which are especially important in disaster situations. Some specific issues that need to be addressed [ 41 ] are connectivity, routing-and-scheduling, communication link models, and data transmission.

Picture reprinted from https://www.google.com/search?q=multirotor+UAV++ground+control+station+images

Platform for drone control from GCS

3.4 Real-time scheduling algorithms

Real-time scheduling aims to complete tasks within specific time constraints and avoiding simultaneous access to resources shared amongst application tasks. To guarantee real-time performance while meeting all timing, precedence and resource usage specifications requires employment of efficient scheduling algorithms supported by accurate schedulability analysis techniques [ 104 ]. Real-time scheduling algorithms can be implemented for uniprocessor or multiprocessor systems [ 105 , 106 , 107 ].

In the context of drone applications, an example could be implementing a flight control system using Arduino Uno or other single processor boards. The Arduino Uno uses the ATMEGA 328P processor (uni-processor), whereas embedded computers like the Rasberry-Pi uses a quad core ARM Cortex-A72 processor (multi-processor). Scheduling algorithms can be broadly divided into two major subsets: offline scheduling and online scheduling algorithms [ 104 ]. In offline scheduling algorithms, task scheduling is carried out before system execution, also known as pre-run time scheduling. The scheduling information is then employed during run-time. The YDS algorithm (named after the author) [ 108 ], which schedules tasks according to earliest deadline first (EDF) precedence [ 109 ] is an example of an offline scheduling algorithm. By contrast, online scheduling algorithms schedule tasks at run-time.An online scheduling algorithm that encoporates event-driven and periodic rolling strategies (EDPRS) is discussed in [ 110 ].

4 Types of controllers

UAV control requires an accurate and robust controller for altitude as well as velocity-and-heading [ 111 ].The altitude controller drives the UAV to fly at the desired altitude, including landing and take-off stages. The heading and velocity control enables UAV to fly through desired waypoints [ 112 ]. To achieve the above control requirements, different control strategies such as Fuzzy Logic,Linear Quadratic Regulator (LQG), Sliding Mode Control (SMC), Proportional Integral Derivative (PID), Neural Network (NN), e.t.c can be used. Robust control systems have been widely developed to address parametric uncertainties and external disturbance. In case of multirotor UAVs uncertainties arising from propeller rotation, blades flapping, change in propeller rotational speed and center of mass position dictates the need for a robust nonlinear controller [ 113 ]. In [ 113 ] robustness as well as compensation forsysten nonlinearities was adresses by combinig the nonlinear sliding mode control (SMC), robust backstepping controller and a nonlinear disturbance observer (NDO). The backstepping controller stabilised translational movement while the SMC controlled the rotational movement of the quadrotor.

The NDO provided all the estimates of disturbances ensuring robustness of the feedback controls. The PID controller was compared with a neural network controller, specifically the direct inverse control neural network (DIC-ANN) in [ 114 ]. The comparison was done in simulation, where both controllers were excited with the same reference altitude reference input and their performances plotted together.The simulation aimed to mimic a quadrotor flight in four phases comprising take-off and climb phase at \(0~<~t<~10~s\) , hovering phase at \(10~<~t~<20~s\) , climb in ramp phase at \(20~<~t<~22.5~s\) , and lastly the final altitude phase at \(22.5~<~t<~50~s\) . The comparison results showed that the DIC-ANN performed better than the PID controller in handling quadrotor altitude dynamics.Also at hovering conditions the DIC-ANN exhibited less steady state error as compared to the PID controller and the transient oscillations damped faster with the DIC-ANN showing that it handles nonlinearities better than the PID controller.

PID controllers are widely used in autopilots due to their ease of implementation, how ever they have limitations when operating in unpredictable and harsh environments. In [ 115 ] the performance of and acuracy of an attitude controller was investigated. The attitude controller is a neural network (NN) based controller trained through reinforcement learning (RL) state of the art algorithms, the Deep Deterministic Policy Gradient (DDPG), Trust Region Pocy Optimisation (TRPO), and the Proximal Policy Optimisation (PPO). The NN controller performance was compared to the performance of a PID controller to determine the appropriacy of NN controller in high precision, time-critical flight control. The contoller performance was evaluated in simulation using GYMFC environment. The results showed that RL can trail accurate attitude attitude controllers, also the controller trained with PPO outperformed a fully tuned PID controller on almost every metric.

The linear quadratic regulation (LQR) optimal control algorithm operates a dynamic system by minimizing a suitable cost function [ 79 ]. When the LQR is used with linear quadratic estimator (LQE) and Kalman filter, it is then referred to as the linear quadratic Gaussian (LQG) The LQG was applied in [ 116 ] for altitude control of a quadrotor micro aerial vehicles (MAVs). Ignoring air resistance, the linearized model for altitude control problem was obtained as ( 3 ), the state space model is represented by ( 4 ) , while the cost function is given by ( 5 ), also refered to in [ 116 ] as the quadratic form creterion. The control objective is to determine the control input U ( t ) to minimise cost function [ 79 ].

The linear Quadratic regulator with and integral with an integral term (LQTI) and a model predictive controller were employed to develop an automatic carrier landing system for a UAV [ 117 ]. The LQTI was applied to the coupled multi-input multi-output (MIMO) UAV dynamic model to reduce steady stare error while the model predictive controller was applied to the final phase landing of the UAV. Automatic carrier landing was performed sequentially by the two controllers. The LQTI controller was applied up to a few seconds before touch down followed by the MPC controller during the final stage of landing. The controller was verified via simulations on HSS Hydro toolbox. Simulation results indicated that the proposed carrier landing system can improve landing accuracy. The performance of the controllers indicted that the LQTI is suitable for calm sea environments while the MPC performs better even in rough sea environments [ 117 ]. Some implementations for UAV control employ the sliding-mode control (SMC) strategy. Sliding-mode control is a nonlinear control method that that utilises a high-frequency switching control signal to the system to command it to slide along a prescribed sliding manifold [ 118 , 119 ]. It encompasses a broad range of varying fields, from pure mathematical problems to application aspects [ 120 ] (Fig. 11 ).

An SMC based fault tolerant control design for underactuated UAVs was implemented on a quadrotor in [ 121 ]. The design approach separated system dynamics into two sub-systems, a fully actuated and an under-actuated subsystem. A Nonsingular Fast Terminal Sliding Mode Controller (NFTSMC) was then designed for the fully actuated subsystem, the Under-actuated Sliding Mode Controller (USSMC) was then derived for the under-actuated subsystem. The controller performance, on a quadrotor platform, demonstrated excellent robustness to actuator faults, disturbances. It had fast convergence and high precision tracking. Herrera et al. designed a sliding-mode controller and applied it in simulation of a quadrotor. They considered a PD sliding surface for vertical take-off and landing. Broad coverage of control algorithms for quadrotors can be found in [ 79 , 122 , 123 , 124 ]. Figures  12 , 13 and 14 shows the PID, LQG, and SMC controllers applied to a quadrotor respectively.

Picture reprinted [ 127 ]

Drone path planning from start 1 and Start 2 to Goal, shortest path taken from both starting points

5 Path planning

Missions of UAVs usually involve travelling from some initial point to a goal point [ 125 , 126 ]. A mission requires generating a path for the UAV to follow. Path planning is one of the main aspects of autonomous navigation [ 127 ]. The path planning problem is to produce a path or set of waypoints for the drone to follow while taking into account the environmental and physical constraints of the drone in order to achieve a collision free flight [ 128 , 129 ]. This is obstacle avoidance while executing the the UAV’s mission. Figure  11 depicts drone paths from start to goal position for two drones launched from different locations, each calculating its best path to reach the goal position.

In the literature pertinent to UAV path planning, several algorithms for measuring distances to obstacles and calculations of the drone’s path are suggested [ 130 , 131 , 132 ]. An optimal flight path planning mechanism to determine the best path of the UAV was developed in [ 133 ]. Consideration of environmental information such as geographical topology,location dependent wireless communication channel statistics and flight risk, sensor node deployment and worth of sensing information for different sensor types was made. The implementation aimed at determining the best path to maximise the value of gathered sensing information as well as to minimise flying time, energy consumption, and UAV operational risks. In [ 127 ], 3D propagation approximate Euclidean distance transformation algorithm was formulated to achieve safe path planning by calculating a 3D buffer around the obstacles. The algorithm prevents the drone from flying too close to obstacles by setting the minimal distance from obstacles according to the size of the drone. The algorithm is also used for drone path planning in [ 127 ]. It is worth noting that current techniques for UAVs path planning are application dependent. Different applications require different path-planning approaches.

A method to enhance massive unmanned aerial vehicles for mission critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting) is investigated in [ 134 ]. The method aims to achieve UAV fast travel while avoiding inter-UAV collision while executing their mission. The path planning problem is tackled by exploiting a mean-field game (MFG) theoretic control method. The method requires UAV state exchange only once at launch, thereafter each UAV controls its acceleration by locally solving two partial differential equations, the Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Due to high computational burden posed by solving the partial differential equations, two machine learning models were used to approximate the solutions of the HJB and the FPK. The performance of the proposed method was validated on simulation, showing that the mean-field game method guarantees UAV collision avoidance. Also for the proposed approach, the effectiveness of the mean field game method is determined by the level of the HJB and FPK training.

Block diagram of PID controller applied to a quadrotor [ 79 ]

Block diagram of LQG controller applied to a quadrotor [ 79 ]

Block diagram of an SMC controller applied to a quadrotor [ 79 ]

6 UAV real-time control implementation

In order to implement real-time control for UAVs, tasks have to be defined. An RTOS is required for tasks scheduling, inter-task communication, and management of available resources such memory, and power consumption [ 135 , 136 , 137 ]. Each task is allocated a memory space, called a stack, in the microprocessor. This is enabled by the RTOS kernel’s support for multi-threading [ 138 , 139 ]. Scheduling and prioritisation of tasks, as well as the update frequency of the sensors providing essential data for task execution, ensure that application time constraints are met [ 140 ]. In [ 141 ], an embedded RTOS (RT-Thread) is applied to a quadcopter to address problems of real-time response, heavy workload and difficulty in control. Practical tests in this work indicated that quadcopter control system based on RT-Thread responded real-timely, and ensured smooth flight with a PID control algorithm.

The application tasks defined in this work are attitude information acquisition, attitude information fusion, and PID control. The latter is for quadcopter control. The application task is developed on top of RT-Thread RTOS running on STM32F407VGT6 microprocessor. The processor is equipped with high-performance ARM Cortex-M4 core with maximum system frequency of 168MHz, an FPU (floating-point unit), 1 Mbyte of flash, and 192 Kbytes of SRAM. It has peripherals such as ADC, SPI, USART, controller area network (CAN) bus, DMA, etc. High operating frequencies and high-speed memory provide high computational power to enable quadcopter complex calculations to be performed. Also additional peripherals reduce the need for external IC and reduce computational burden from the microprocessor. The implementation in [ 142 ] uses a dual processor configuration.

One processor is used for telemetry and another for control of a custom quadcopter used as a test-bed. The telemetry processor executes software tasks such as communicating reconfiguration and monitoring data with the GCS, data collection from sensors, and wirelessly transmitting data to the GCS. The tasks are managed by \(\mu \) C/OS-II™, an RTOS. The control processor runs the PID controller algorithm for the quadcopter stabilization and navigation. This task was achieved through several tasks allocated to the control processor. Tasks include reading GPS, compass, IMU, and altitude sensor data received from the telemetry processor. Other tasks include implementation of the roll, pitch, yaw, and altitude PID control loops, and communicating reconfiguration and monitoring data with the telemetry processor via CAN bus. Figure  15 shows the PID controllers used in the implementation.

Picture reprinted from [ 142 ]

PID control loops implemented by the control processor

7 Essential components for UAV real-time applications

7.1 real-time operating system (rtos).

The literature pertaining to real-time implementation of drone control systems is relatively limited, and the number of reported studies on UAV scheduling has been minimal [ 143 ]. The main feature of real-time implementation in drones control is that an embedded RTOS, also referred to as UAV operation system in some literature, is required [ 67 , 144 ]. The RTOS provides a real-time kernel on which the control program running on a micro-controller is implemented. The real-time kernel guarantees application tasks meet their time constraints by employing the UAV scheduling system [ 143 ]. Consequently, a Real-Time Operating System (RTOS) that provides operating environments for various mission services on UAVs is crucial [ 145 ]. The commonly used RTOS for UAVs is FreeRTOS, and an empirical study of this RTOS was conducted in [ 145 ]. The study looked at aspects such as functionality changes during the evolution of FreeRTOS. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0 were considered.

7.2 Microcontroller

The microcontroller is the UAV onboard processing unit for UAV computations and UAV state monitoring [ 146 , 147 ]. It is selected such that it matches application task requirements. Considerations such as computational speeds and communication with onboard sensors have to be made. Palossi et al. [ 146 ] extended the hardware and software of a 27 grams nano-size, commercial off-the-shelf (COTS) quadrotor, the crazyflie 2.0, to achieve object tracking capability. The quadrotor platform consists the STM32F405 microcontroller as the main onboard processing unit, the Nordic nRF51 module for wireless communication. The STM32 is an ARM Cortex-M4F microcontroller, operating at 168MHz. The on-board sensing is performed by a 9-axis IMU, the MPU-9250 with a gyroscope, an accelerometer, a magnetometer, and an ST LPS25H pressure sensor with a typical accuracy of \(\pm 1\) meter. The vehicle is powered by a 240mAh Li-Po battery.

Reprinted from [ 150 ]

Sensors connected to microcontroller

7.3 Sensors and actuators

In UAV applications several sensors and actuators are connected to the microprocessor for UAV control. Table 1 highlights the vital components for real-time implementation of UAV control, the table also lists various sensors used. Figure  16 shows the UAV onboard sensors used in a fire fighting remote-sensing system in [ 150 ]. Various sensors as well as the overall connection network is depicted.

8 Conclusion

Real-time control of drones requires an embedded RTOS for implementation. The RTOS provides facilities such as multi-threading, scheduling and priority assignment. These support real-time response of the drone control system to feedback from GPS and IMU. The drone control system subsequently apply the corresponding motor speeds to achieve the desired drone’s movements. Multitasking enables tasks, such as position and orientation feedback, path-planning, and control implementation to run in parallel. This facilitates real-time response of the drone. Tasks may need results from other tasks for their computations. Scheduling and prioritisation of tasks ensures that at any point in time critical tasks are given computational resources by the microprocessor. For example obstacle avoidance is the highest priority task to ensure that the drone does not collide with other drones as well as other obstacles.

Kanellakis C, Nikolakopoulos G (2017) Survey on computer vision for UAVs: current developments and trends. J Intell Robot Syst 87(1):141–168

Google Scholar  

Han D, Gwak DY, Lee S (2020) Noise prediction of multi-rotor UAV by RPM fluctuation correction method. J Mech Sci Technol 34:1429–1443

Fotouhi A, Qiang H, Ding M, Hassan M, Giordano LG, Garcia-Rodriguez A, Yuan J (2019) Survey on UAV cellular communications: practical aspects, standardization advancements, regulation, and security challenges. IEEE Commun Surv Tutor 21(4):3417–3442

Atyabi A, Mahmoud-Zadeh S, Nefti-Meziani S (2018) Current advancements on autonomous mission planning and management systems: an AUV and UAV perspective. Annu Rev Control 46:196–215

Jawhar I, Mohamed N, Al-Jaroodi J, Agrawal DP, Zhang S (2017) Communication and networking of UAV-based systems: classification and associated architectures. J Netw Comput Appl 84:93–108

Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I (2020) A compilation of UAV applications for precision agriculture. Comput Netw 172:107148

Balasingam M (2017) Drones in medicine—the rise of the machines. Int J Clin Pract 71(9):1–4

Parks L (2016) Drones, vertical mediation, and the targeted class. Fem Stud 42(1):227–235

Abbasi Y, Moosavian SAA, Novinzadeh AB (2017) Vision-based formation control of aerial robots in the presence of sensor failure. J Mech Sci Technol 31(3):1413–1426

Rao B, Gopi AG, Maione R (2016) The societal impact of commercial drones. Technol Soc 45:83–90

Nex F, Remondino F (2014) UAV for 3D mapping applications: a review. Appl Geomat 6(1):1–15

Menouar H, Guvenc I, Akkaya K, Uluagac AS, Kadri A, Tuncer A (2017) UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun Mag 55(3):22–28

Williams HA, Jones MH, Nejati M, Seabright MJ, Bell J, Penhall ND, Barnett JJ, Duke MD, Scarfe AJ, Ahn HS et al (2019) Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosyst Eng 181:140–156

Park D, Shaear M, Chen YH, Russell JO, Kim HY, Tufano RP (2019) Transoral robotic thyroidectomy on two human cadavers using the intuitive da vinci single port robotic surgical system and CO2 insufflation: preclinical feasibility study. Head Neck 41(12):4229–4233

Jamisola RS, Maciejewski AA, Roberts RG (2006) Failure-tolerant path planning for kinematically redundant manipulators anticipating locked-joint failures. IEEE Trans Rob 22(4):603–612

Kim MJ, Kondak K, Ott C (2018) A stabilizing controller for regulation of UAV with manipulator. IEEE Robot Autom Lett 3(3):1719–1726

Muscio G, Pierri F, Trujillo MA, Cataldi E, Antonelli G, Caccavale F, Viguria A, Chiaverini S, Ollero A (2017) Coordinated control of aerial robotic manipulators: theory and experiments. IEEE Trans Control Syst Technol 26(4):1406–1413

Ramon Soria P, Arrue BC, Ollero A (2017) Detection, location and grasping objects using a stereo sensor on UAV in outdoor environments. Sensors 17(1):103

Xiao B, Yin S (2018) Exponential tracking control of robotic manipulators with uncertain dynamics and kinematics. IEEE Trans Ind Inf 15(2):689–698

Yang C, Jiang Y, Na J, Li Z, Cheng L, Su CY (2018) Finite-time convergence adaptive fuzzy control for dual-arm robot with unknown kinematics and dynamics. IEEE Trans Fuzzy Syst 27(3):574–588

Jamisola RS Jr, Mastalli C, Ibikunle F (2016) Modular relative jacobian for combined 3-arm parallel manipulators. Int J Mech Eng Robot Res 5(2):90–95

Jamisola RS, Kormushev PS, Roberts RG, Caldwell DG (2016) Task-space modular dynamics for dual-arms expressed through a relative jacobian. J Intell Robot Syst 83(2):205–218

Chen S, Laefer DF, Mangina E (2016) State of technology review of civilian UAVs. Recent Patents Eng 10(3):160–174

Dupont QF, Chua DK, Tashrif A, Abbott EL (2017) Potential applications of UAV along the construction’s value chain. Procedia Eng 182:165–173

Otto A, Agatz N, Campbell J, Golden B, Pesch E (2018) Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: a survey. Networks 72(4):411–458

MathSciNet   Google Scholar  

Gaffey C, Bhardwaj A (2020) Applications of unmanned aerial vehicles in cryosphere: latest advances and prospects. Remote Sens 12(6):1–40

Garaba SP, Dierssen HM (2018) An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro-and microplastics. Remote Sens Environ 205:224–235

Zhang J, Yang C, Song H, Hoffmann WC, Zhang D, Zhang G (2016) Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sens 8(3):257

Koc CB, Osmond P, Peters A, Irger M (2018) Understanding land surface temperature differences of local climate zones based on airborne remote sensing data. IEEE J Sel Top Appl Earth Obs Remote Sens 11(8):2724–2730

Bhardwaj A, Sam L, Martín-Torres FJ, Kumar R et al (2016) UAVs as remote sensing platform in glaciology: present applications and future prospects. Remote Sens Environ 175:196–204

Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, Xu B, Yang X, Zhu D, Zhang X et al (2017) Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci 8:1–26

Näsi R, Honkavaara E, Blomqvist M, Lyytikäinen-Saarenmaa P, Hakala T, Viljanen N, Kantola T, Holopainen M (2018) Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban For Urban Green 30:72–83

Mohammed M, Hazairin NA, Al-Zubaidi S, Sairah AK, Mustapha S, Yusuf E (2020) Toward a novel design for coronavirus detection and diagnosis system using IoT based drone technology. Int J Psychosoc Rehabil 24(7):2287–2295

Ebeid E, Skriver M, Terkildsen KH, Jensen K, Schultz UP (2018) A survey of open-source UAV flight controllers and flight simulators. Microprocess Microsyst 61:11–20

Zhang G, Hsu LT (2018) Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system. Aerosp Sci Technol 80:368–380

Zhou L, Zhang J, She H, Jin H (2019) Quadrotor UAV flight control via a novel saturation integral backstepping controller. Automatika 60(2):193–206

Silvagni M, Tonoli A, Zenerino E, Chiaberge M (2017) Multipurpose UAV for search and rescue operations in mountain avalanche events. Geomat Nat Hazards Risk 8(1):18–33

Khalesi MH, Salarieh H, Foumani MS (2019) Dynamic modeling, control system design and MIL–HIL tests of an unmanned rotorcraft using novel low-cost flight control system. Iran J Sci Technol Trans Mech Eng 1–20

Corrigan F (2018) How do drones work and what is drone technology. DroneZon. 24 June 2018

Vasić Z, Maksimović S, Georgijević D (2018) Applied integrated design in composite UAV development. Appl Compos Mater 25(2):221–236

Yanmaz E, Yahyanejad S, Rinner B, Hellwagner H, Bettstetter C (2018) Drone networks: communications, coordination, and sensing. Ad Hoc Netw 68:1–15

Aasen H, Burkart A, Bolten A, Bareth G (2015) Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J Photogramm Remote Sens 108:245–259

Zhong Y, Wang X, Xu Y, Wang S, Jia T, Hu X, Zhao J, Wei L, Zhang L (2018) Mini-UAV-borne hyperspectral remote sensing: from observation and processing to applications. IEEE Geosci Remote Sens Mag 6(4):46–62

Natesan S, Armenakis C, Benari G, Lee R (2018) Use of UAV-borne spectrometer for land cover classification. Drones 2(2):16

Li B, Jiang Y, Sun J, Cai L, Wen CY (2016) Development and testing of a two-UAV communication relay system. Sensors 16(10):1696

She C, Liu C, Quek TQ, Yang C, Li Y (2019) Ultra-reliable and low-latency communications in unmanned aerial vehicle communication systems. IEEE Trans Commun 67(5):3768–3781

Rafifandi R, Asri DL, Ekawati E, Budi EM (2019) Leader-follower formation control of two quadrotor UAVs. SN Appl Sci 1(6):539

Pastor E, Lopez J, Royo P (2007) UAV payload and mission control hardware/software architecture. IEEE Aerosp Electron Syst Mag 22(6):3–8

González-deSantos LM, Martínez-Sánchez J, González-Jorge H, Navarro-Medina F, Arias P (2020) UAV payload with collision mitigation for contact inspection. Autom Constr 115:103200

González-de Santos L, Martínez-Sánchez J, González-Jorge H, Novo A, Arias P (2019) First approach to UAV-based contact inspection: a smart payload for navigation in the neighbourhood of structures. ISPAr 4213:323–328

Sanchez-Lopez JL, Pestana J, De La Puente P, Campoy P (2016) A reliable open-source system architecture for the fast designing and prototyping of autonomous multi-UAV systems: simulation and experimentation. J Intell Robot Syst 84(1–4):779–797

Sabikan S, Nawawi S (2016) Open-source project (OSPs) platform for outdoor quadcopter. J Adv Res Des 24:13–27

Yang K, Yang GY, Fu SIH (2020) Research of control system for plant protection UAV based on pixhawk. Procedia Comput Sci 166:371–375

Bingler A, Mohseni K (2017) Dual radio autopilot system for lightweight, swarming micro/miniature aerial vehicles. J Aerosp Inf Syst 14(5):293–306

Garberoglio L, Meraviglia M, Pose CD, Giribet JI, Mas I (2018) Choriboard III: a small and powerful flight controller for autonomous vehicles. In: 2018 Argentine Conference on Automatic Control (AADECA), Buenos Aires, Argentina. IEEE, pp 1–6

Zareb M, Nouibat W, Bestaoui Y, Ayad R, Bouzid Y (2020) Evolutionary autopilot design approach for UAV quadrotor by using GA. Iran J Sci Technol Trans Electr Eng 44(1):347–375

González JMF, Padilla P, Valenzuela-Valdes JF, Padilla JL, Sierra-Perez M (2017) An embedded lightweight folded printed quadrifilar helix antenna: UAV telemetry and remote control systems. IEEE Antennas Propag Mag 59(3):69–76

Wang H, Wang J, Chen J, Gong Y, Ding G (2018) Network-connected UAV communications: potentials and challenges. China Commun 15(12):111–121

Bhola R, Krishna NH, Ramesh K, Senthilnath J, Anand G (2018) Detection of the power lines in UAV remote sensed images using spectral–spatial methods. J Environ Manag 206:1233–1242

Cui JQ, Lai S, Dong X, Chen BM (2016) Autonomous navigation of UAV in foliage environment. J Intell Robot Syst 84(1–4):259–276

Rawashdeh NA, Rawashdeh OA, Sababha BH (2017) Vision-based sensing of UAV attitude and altitude from downward in-flight images. J Vib Control 23(5):827–841

Chen S, Duan H, Deng Y, Li C, Zhao G, Xu Y (2017) Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor. Opt Eng 56(12):124105

Cai X, Hsu H, Chai H, Ding L, Wang Y (2019) Multi-antenna GNSS and INS integrated position and attitude determination without base station for land vehicles. J Navig 72(2):342–358

Kumar GA, Patil AK, Patil R, Park SS, Chai YH (2017) A LiDAR and IMU integrated indoor navigation system for UAVs and its application in real-time pipeline classification. Sensors 17(6):1268

Kuutti S, Fallah S, Katsaros K, Dianati M, Mccullough F, Mouzakitis A (2018) A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet Things J 5(2):829–846

Noor-A-Rahim M, Khyam MO, Ali GMN, Liu Z, Pesch D, Chong PH (2019) Reliable state estimation of an unmanned aerial vehicle over a distributed wireless IoT network. IEEE Trans Reliab 68(3):1061–1069

Hujja RM, Sumiharto R, Setyawan Bekti W (2018) Realtime operating system implementation on AVR XMEGA for unmanned aerial vehicle autopilot. Int J Adv Res Sci Eng Technol 5:5762–5768

Al-Mashhadani MA (2019) Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty. Meas Control 52(9–10):1264–1271

Wright WC, Wilkinson BE, Cropper WP Jr (2017) Estimating GPS signal loss in a natural deciduous forest using sky photography. Pap Appl Geogr 3(2):119–128

Lu Y, Xue Z, Xia GS, Zhang L (2018) A survey on vision-based UAV navigation. Geo-Spat Inf Sci 21(1):21–32

Guo H, Hong H (2019) Research on filtering algorithm of MEMS gyroscope based on information fusion. Sensors 19(16):3552

Kwak J, Sung Y (2018) Autonomous UAV flight control for GPS-based navigation. IEEE Access 6:37947–37955

Capello E, Guglieri G, Ristorto G (2017) Guidance and control algorithms for mini UAV autopilots. Aircr Eng Aerosp Technol 89(1):133–144

Vanegas F, Gonzalez F (2016) Enabling UAV navigation with sensor and environmental uncertainty in cluttered and GPS-denied environments. Sensors 16(5):1–17

Chao H, Cao Y, Chen Y (2010) Autopilots for small unmanned aerial vehicles: a survey. Int J Control Autom Syst 8(1):36–44

Dapper e Silva T, Cabreira V, De Freitas EP (2018) Development and testing of a low-cost instrumentation platform for fixed-wing UAV performance analysis. Drones 2(2):19

Hassanalian M, Abdelkefi A (2017) Classifications, applications, and design challenges of drones: a review. Prog Aerosp Sci 91:99–131

Yang H, Lee Y, Jeon SY, Lee D (2017) Multi-rotor drone tutorial: systems, mechanics, control and state estimation. Intell Serv Robot 10(2):79–93

Zulu A, John S (2014) A review of control algorithms for autonomous quadrotors. Open J Appl Sci 4:547–556

Noordin A, Basri MM, Mohamed Z, Abidin AZ (2017) Modelling and PSO fine-tuned PID control of quadrotor UAV. Int J Adv Sci Eng Inf Technol 7(4):1367–1373

Gupte S, Mohandas PIT, Conrad JM (2012) A survey of quadrotor unmanned aerial vehicles. In: 2012 Proceedings of IEEE Southeastcon. IEEE, pp 1–6

Orsag M, Bogdan S (2012) Influence of forward and descent flight on quadrotor dynamics. Recent Adv Aircr Technol 141–156

Emran BJ, Najjaran H (2018) A review of quadrotor: an underactuated mechanical system. Annu Rev Control 46:165–180

Roh MS, Kang BS (2018) Dynamic accuracy improvement of a MEMS AHRS for small UAVs. Int Jo Precis Eng Manuf 19(10):1457–1466

Tang Y, Hu Y, Cui J, Liao F, Lao M, Lin F, Teo RS (2018) Vision-aided multi-UAV autonomous flocking in GPS-denied environment. IEEE Trans Ind Electron 66(1):616–626

Wang W, Wang Y, Dai J, Cao Z (2019) Dynamic soft real-time scheduling with preemption threshold for streaming media. Int J Digit Multimed Broadcast 1–8

Turci LDO (2017) Real-time operating system freertos application for fire alarm project in reduced scale. Int J Comput Digit Syst 6(04):197–204

Nissimagoudar P, Mane V, Shamshuddin K, Gireesha H, Joshi R, Kiran M, Desai B (2016) Integrated course projects in automotive electronics and RTOS. J Eng Educ Transform 30(2):71–78

Macher G, Atas M, Armengaud E, Kreiner C (2015) Automotive real-time operating systems: a model-based configuration approach. ACM Sigbed Rev 11(4):67–72

Stankovic JA, Rajkumar R (2004) Real-time operating systems. Real-Time Syst 28(2–3):237–253

MATH   Google Scholar  

Andersson B, Kim H, Niz DD, Klein M, Rajkumar R, Lehoczky J (2018) Schedulability analysis of tasks with corunner-dependent execution times. ACM Trans Embed Comput Syst (TECS) 17(3):1–29

Kang Y, Joo W, Lee S, Shin D (2017) Priority-driven spatial resource sharing scheduling for embedded graphics processing units. J Syst Architect 76:17–27

Miné A (2017) Static analysis of embedded real-time concurrent software with dynamic priorities. Electron Notes Theor Comput Sci 331:3–39

Hellaoui H, Bekkouche O, Bagaa M, Taleb T (2018) Aerial control system for spectrum efficiency in UAV-to-cellular communications. IEEE Commun Mag 56(10):108–113

Mcguire K, Croon GD, Wagter CD, Tuyls K, Kappen H (2017) Efficient optical flow and stereo vision for velocity estimation and obstacle avoidance on an autonomous pocket drone. IEEE Robot Autom Lett 2(2):1070–1076. https://doi.org/10.1109/lra.2017.2658940

Article   Google Scholar  

Sani MF, Karimian G (2017) Automatic navigation and landing of an indoor AR. drone quadrotor using AruUo marker and inertial sensors. In: 2017 International Conference on Computer and Drone Applications (IConDA), Kuching, Sarawak, Malaysia. IEEE, pp 102–107

Artieda J, Sebastian JM, Campoy P, Correa JF, Mondragón IF, Martínez C, Olivares M (2009) Visual 3D slam from UAVs. J Intell Robot Syst 55(4–5):299

Harmat A, Trentini M, Sharf I (2015) Multi-camera tracking and mapping for unmanned aerial vehicles in unstructured environments. J Intell Robot Syst 78(2):291–317

Molaei F, Rahimi E, Siavoshi H, Afrouz SG, Tenorio V (2020) A comprehensive review on internet of things (IoT) and its implications in the mining industry. Am J Eng Appl Sci 13(3):499–515

Zhao B, Chen X, Zhao X, Jiang J, Wei J (2018) Real-time UAV autonomous localization based on smartphone sensors. Sensors 18(12):1–17

Astudillo A, Muñoz P, Álvarez F, Rosero E (2017) Altitude and attitude cascade controller for a smartphone-based quadcopter. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA. IEEE, pp 1447–1454

Hayajneh M, Melega M, Marconi L (2018) Design of autonomous smartphone based quadrotor and implementation of navigation and guidance systems. Mechatronics 49:119–133

Carlson DF, Rysgaard S (2018) Adapting open-source drone autopilots for real-time iceberg observations. MethodsX 5:1059–1072

Roy SK, Devaraj R, Sarkar A, Maji K, Sinha S (2020) Contention-aware optimal scheduling of real-time precedence-constrained task graphs on heterogeneous distributed systems. J Syst Architect 105:1–14

Tobita T, Kasahara H (2002) A standard task graph set for fair evaluation of multiprocessor scheduling algorithms. J Sched 5(5):379–394

MathSciNet   MATH   Google Scholar  

Singh A, Ekberg P, Baruah S (2019) Uniprocessor scheduling of real-time synchronous dataflow tasks. Real-Time Syst 55(1):1–31

Panda SK, Jana PK (2015) Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 71(4):1505–1533

Albers S (2009) Algorithms for energy saving. In: Albers S, Alt H, Näher S (eds) Efficient algorithms: essays dedicated to Kurt Mehlhorn on the occasion of his 60th birthday. Springer, Berlin, pp 173–186

Gerards MET, Hurink JL, Hölzenspies PKF (2016) A survey of offline algorithms for energy minimization under deadline constraints. J Sched 19(1):3–19. https://doi.org/10.1007/s10951-015-0463-8

Article   MathSciNet   MATH   Google Scholar  

Chen H, Zhu J, Zhang Z, Ma M, Shen X (2017) Real-time workflows oriented online scheduling in uncertain cloud environment. J Supercomput 73(11):4906–4922

Li H, Li C, Li H, Li Y, Xing Z (2017) An integrated altitude control design for a tail-sitter UAV equipped with turbine engines. IEEE Access 5:10941–10952

Zeng Y, Xu X, Zhang R (2018) Trajectory design for completion time minimization in UAV-enabled multicasting. IEEE Trans Wirel Commun 17(4):2233–2246

Labbadi M, Cherkaoui M (2019) Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV. Aerosp Sci Technol 93:105306

Muliadi J, Kusumoputro B (2018) Neural network control system of UAV altitude dynamics and its comparison with the PID control system. J Adv Transp 3823201

Koch W, Mancuso R, West R, Bestavros A (2019) Reinforcement learning for UAV attitude control. ACM Trans Cyber-Phys Syst 3(2):1–21

Wang W, Ma H, Xia M, Weng L, Ye X (2013) Attitude and altitude controller design for quad-rotor type MAVs. Math Probl Eng 1–10

Koo S, Kim S, Suk J, Kim Y, Shin J (2018) Improvement of shipboard landing performance of fixed-wing UAV using model predictive control. Int J Control Autom Syst 16(6):2697–2708

Jing C, Xu H, Niu X, Song X (2019) Adaptive nonsingular terminal sliding mode control for attitude tracking of spacecraft with actuator faults. IEEE Access 7:31485–31493

Chen F, Jiang R, Zhang K, Jiang B, Tao G (2016) Robust backstepping sliding-mode control and observer-based fault estimation for a quadrotor UAV. IEEE Trans Ind Electron 63(8):5044–5056

Alsmadi YM, Utkin V, Haj-ahmed MA, Xu L (2018) Sliding mode control of power converters: DC/DC converters. Int J Control 91(11):2472–2493

Mallavalli S, Fekih A (2018) An SMC-based fault tolerant control design for a class of underactuated unmanned aerial vehicles. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, New Zealand. IEEE, pp 152–155

Mo H, Farid G (2019) Nonlinear and adaptive intelligent control techniques for quadrotor UAV—a survey. Asian J Control 21(2):989–1008

Cajo R, Mac TT, Plaza D, Copot C, De Keyser R, Ionescu C (2019) A survey on fractional order control techniques for unmanned aerial and ground vehicles. IEEE Access 7:66864–66878

Amin R, Aijun L, Shamshirband S (2016) A review of quadrotor UAV: control methodologies and performance evaluation. Int J Autom Control 10(2):87–103

Atencia CR, Del Ser J, Camacho D (2019) Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning. Swarm Evolut Comput 44:480–495

Ramirez-Atencia C, Bello-Orgaz G, R-Moreno MD, Camacho D (2017) Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput 21(17):4883–4900

Li F, Zlatanova S, Koopman M, Bai X, Diakité A (2018) A universal path planning for an indoor drone. Autom Constr 95:275–283. https://doi.org/10.1016/j.autcon.2018.07.025

Chen Y, Luo G, Mei Y, Yu J, Su X (2016) UAV path planning using artificial potential field method updated by optimal control theory. Int J Syst Sci 47(6):1407–1420

Lin Y, Saripalli S (2017) Sampling-based path planning for UAV collision avoidance. IEEE Trans Intell Transp Syst 18(11):3179–3192

Li J, Deng G, Luo C, Lin Q, Yan Q, Ming Z (2016) A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems. IEEE Trans Veh Technol 65(12):9585–9596

Chen Y, Yu J, Mei Y, Wang Y, Su X (2016) Modified central force optimization (MCFO) algorithm for 3D UAV path planning. Neurocomputing 171:878–888

Silva Arantes JD, Silva Arantes MD, Motta-Toledo CF, Júnior OT, Williams BC (2017) Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int J Artif Intell Tools 26(1):1760008

Yang Q, Yoo SJ (2018) Optimal UAV path planning: sensing data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms. IEEE Access 6:13671–13684

Shiri, H, Park J, Bennis M (2019) Massive autonomous UAV path planning: a neural network based mean-field game theoretic approach. In: 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, Hawaii. IEEE, pp 1–6

Musaddiq A, Zikria YB, Hahm O, Yu H, Bashir AK, Kim SW (2018) A survey on resource management in IoT operating systems. IEEE Access 6:8459–8482

Zagan I, Găitan VG (2019) Hardware RTOS: custom scheduler implementation based on multiple pipeline registers and MIPS32 architecture. Electronics 8(2):211

Chandane MP (2016) Real time operating systems: a complete overview. Int J Electr Electron Eng (IJEEE) [e-journal] 8(1):361–365

Li Y, Matsubara Y, Takada H (2018) A comparative analysis of RTOS and linux scalability on an embedded many-core processor. J Inf Process 26:225–236

Zaykov PG, Kuzmanov G, Molnos A, Goossens K (2016) RTOS acceleration in an MPSoC with reconfigurable hardware. Comput Electr Eng 53:89–105

Dietrich C, Lohmann D (2017) OSEK-V: application-specific RTOS instantiation in hardware. ACM SIGPLAN Notices 52(5):111–120

Bonarini A, Matteucci M, Migliavacca M, Rizzi D (2014) R2p: an open source hardware and software modular approach to robot prototyping. Robot Auton Syst 62(7):1073–1084

Sababha B, Yang HC, Rawashdeh O (2010) An RTOS-based run-time reconfigurable avionics system for UAVs, Atlanta, Georgia. In: AIAA Infotech@ Aerospace 2010, p 3414

Khosiawan Y, Park Y, Moon I, Nilakantan JM, Nielsen I (2019) Task scheduling system for UAV operations in indoor environment. Neural Comput Appl 31(9):5431–5459

Alvear O, Zema NR, Natalizio E, Calafate CT (2017) Using UAV-based systems to monitor air pollution in areas with poor accessibility. J Adv Transp 2017:1–14

Zheng Z, Guanping X (2019) Evolution analysis of a UAV real-time operating system from a network perspective. Chin J Aeronaut 32(1):176–185

Palossi D, Singh J, Magno M, Benini L (2017) Target following on nano-scale unmanned aerial vehicles. In: 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), Vieste, Italy. IEEE, pp 170–175

Bulka E, Nahon M (2019) Automatic control for aerobatic maneuvering of agile fixed-wing UAVs. J Intell Robot Syst 93(1–2):85–100

Artale V, Collotta M, Milazzo C, Pau G, Ricciardello A (2016) An integrated system for UAV control using a neural network implemented in a prototyping board. J Intell Robot Syst 84(1–4):5–19

Dong M, Chen BM, Cai G, Peng K (2007) Development of a real-time onboard and ground station software system for a UAV helicopter. J Aerosp Comput Inf Commun 4(8):933–955

Sherstjuk V, Zharikova M, Sokol I (2018) Forest fire-fighting monitoring system based on UAV team and remote sensing. In: 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), National Technical University of Ukraine, Kyiv. IEEE, pp 663–668

Download references

Acknowledgements

The authors would like to acknowledge the funding support on this work from the Botswana International University of Science and Technology (BIUST) Drones Project with project number P00015. The authors would also like to thank Boyce Segweni for his help in the preparation of this manuscript.

Author information

Authors and affiliations.

BIUST, Palapye, Botswana

Vemema Kangunde, Rodrigo S. Jamisola Jr. & Emmanuel K. Theophilus

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Vemema Kangunde .

Ethics declarations

Conflict of interest.

All authors declares that they have no conflict of interest.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Kangunde, V., Jamisola, R.S. & Theophilus, E.K. A review on drones controlled in real-time. Int. J. Dynam. Control 9 , 1832–1846 (2021). https://doi.org/10.1007/s40435-020-00737-5

Download citation

Received : 16 October 2020

Revised : 11 November 2020

Accepted : 19 November 2020

Published : 05 January 2021

Issue Date : December 2021

DOI : https://doi.org/10.1007/s40435-020-00737-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Unmanned areal vehicles
• Real-time control
• Real-time operating system
• Global positioning system
• Inertial measurement unit

Advertisement

• Find a journal
• Publish with us
• Track your research

• Skip to main content
• Skip to search
• Skip to footer

無線アクセスポイント2800/3800/4800/1560及びIW6300シリーズに関する重要なお知らせ

Making AI work for you

加速する時代を大きくリードする。シスコの AI が、意味のある、そして目に見える価値を提供します。

AI：その進め方、支え方、守り方

AI の無料トライアルとデモ

AI 関連製品を含め、100 を上回るシスコの製品とポートフォリオ ソリューションを無料でお試しいただけます。

お使いの IT が対象です

Cisco AI Assistant が幅広くデータにアクセスして、より迅速かつ安全でスマートなビジネスをサポートします。

Cisco Full-Stack Observability の AIOps

IT の問題をリアルタイムでインテリジェントに自動解決。必要な操作はワンクリックだけです。

あなたのデータに、新たな一日。

シスコは、特別なスタートを切ります。Splunk がシスコの一員になったことを、ここに発表いたします。

Cisco ESG レポートハブ

環境・社会・ガバナンス（ESG）に対するシスコのイニシアチブ、成果、会社方針に関連する情報とデータを紹介します。

医療法人篠田好生会 篠田総合病院

ミッションクリティカルな電子カルテシステムのネットワークインフラを Cisco Meraki で構築し医療の質の向上を実現

未来のスタジアム、ここに誕生。

シスコは、レアル・マドリードの象徴的存在ともいえるサンティアゴ・ベルナベウ・スタジアムの全面改修を支援しています。未来のファンダムが形作られる様子をご覧ください。

最新の関連情報とイベント

シスコ、2024年版 日本における「働きがいのある会社」第1位に選出.

シスコシステムズ合同会社は、Great Place to Work® Institute Japan（以下 GPTWジャパン）の調査による、2024年版日本における「働きがいのある会社」大規模部門（従業員1,000名以上）において、2年連続で第1位に選出されたことをお知らせします。シスコが第1位に選出されたのは、2018年、2021年、2023年に続き、今回が4回目となります。

Interop 2024基調講演：デジタル化推進に向けて、リーダーが果たすべき責務デジタル化推進に向けて、リーダーが果たすべき責務

2024年6月13日（木）にSenior Vice President and the Global Innovation Officer ガイ・ディードリックが来日し、デジタル化推進に向けて必要な、イノベーションの推進と倫理性の実現について基調講演を行います。

Cisco Sustainability Summit 2024 イベントレポート

企業でサステナビリティ推進やサステナブルな IT 選定などに従事される方々を対象に、ICTを活用したサステナビリティの推進について検討することを目的に開催。そのセッション内容を、ダイジェストレポートの形でご紹介します