research paper on drone technology pdf

A Review and Progress of Research on Autonomous Drone in Agriculture, Delivering Items and Geographical Information Systems (GIS)

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Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control

Commercial and private deployment of airborne drones is revolutionising many ecosystems. To identify critical issues and research gaps, our systematic literature review findings suggest that historic issues such as privacy, acceptance and security are increasingly replaced by operational considerations including interaction with and impacts on other airspace users. Recent incidents show that unrestricted drone use can inflict problems on other airspace users like airports and emergency services. Our review of current regulatory approaches shows a need for further policy and management response to both manage rapid and efficient drone usage growth, and facilitate innovation (e.g. intraurban package delivery), with one promising strategic response being low altitude airspace management (LAAM) systems for all drone use cases.

• • Historic issues such as privacy, acceptance and security increasingly replaced by operational and strategy considerations.
• • The literature on drones is wide and not significantly concentrated in any particular source or to any author/ institution.
• • Drone usage can be categorised into 4 uses: monitoring/inspection/data collection, photography, recreation and logistics.
• • Low altitude airspace management (LAAM) as strategic response for all drone use cases.

1. Introduction

Remote technology and automation have been present for centuries, giving human operators safety from harm and enabling new task functionality (increasing capability of individual operations and capacity of the system). Early examples include fireships, a maritime drone, which were used in navies to destroy other ships remotely. In World Wars 1 and 2, airborne drones were used to disrupt airspace above cities, drop ordinance on enemy territory and as target practice for pilots. Railways have for some time used drone (non-crewed) locomotives to support driver occupied locomotives.

While drones have had a long history in military deployment, their increasingly widespread use in non-military roles requires consideration (e.g., Hodgkinson and Johnston, 2018 ). Though current usage is limited whilst the technology is in the development phase, as they possess significant potential versatility drones may transform the way that logistics services are provided. Their use no doubt will lead to the achievement of new business, social, environmental and other goals ( Atwater, 2015 ). However, it also creates a potentially disruptive scenario as their usage expands out of control and causing problems for other parts of the economic system, as illustrated in the rapidly growing literature presented in this paper.

Interestingly, during the COVID-19 crisis drone potential has been further harnessed, using the people free nature of the technology to modify current service delivery to improve safety and capacity levels, including the delivery of face masks to remote islands in Korea and prescription medicines from pharmacies to retirement villages in Florida. It could be argued that COVID-19 has increased technological advancement in many areas and that perhaps drones represent a revolution in how we transport goods and potentially even ourselves (however that is analysis for a future paper).

In that sense, it is important to note that the use of drones in larger commercial applications is also growing (see, e.g. Bartsch et al., 2016 ), with their deployment in remote work leading to significant cost reductions and capability enhancements (such as in mining, engineering and transport network management contexts and agricultural scanning). Their ability to view large areas at a low cost from altitude provides new viewing aspects and new data acquisition ability (or existing data can be sourced at a large scale at a lower cost) to make decisions and manage operations more effectively. Similarly, airborne photography has entered a new stage of development with operators, both large and small, able to give consumers new imagery that had previously been in the domain of birds only. Besides, the recent spurt of the retail sale of drones for recreational and small-scale commercial purposes has pushed airborne drones into the entertainment space.

However, there is a range of other potential uses. Experience in delivering medical supplies in remote African areas gives a potential preview of their role in urban parcel/package delivery, radically changing the way small deliveries are made in urban areas. Commercial and policymaking efforts are turning to contemplate this future and how airborne drones may need control in such uses. This may have significant impacts, not only on delivery cost but on urban congestion and traffic management issues – should they replace land-based journeys. Being in urban areas, implementation issues will arise that require consideration, given the greater risks involved.

While there have been earlier reviews (e.g. a techno-ethical one, Luppicini and So, 2016 ), the commercial use of drones is yet to be written about in any significant volume in the management literature. Preliminary issues like privacy/security received the required attention, given the potential for drones to peer (visually or audially, and intentionally or not) into areas that were previously easy to guard. With increased use, the focus has moved to the engineering literature where a range of computer, materials and design issues are being discussed. Recently, the management literature has begun to case study how drones are used in current commercial contexts, and more importantly, has begun to consider the broader role that drones may play in the logistics industry. What is missing, in our view, is a clear understanding as to where to next, given that increased use cases and traffic volumes might not only significantly disturb other airspace users but also bring the drone ecosystem itself to a standstill (uncontrolled chaos scenario). We aim to investigate whether the emerging body of literature can provide sufficient answers and solutions or at least trending ideas on how to provide drone use with a framework that allows this evolving industry to continue growing at a rapid pace and also to innovatively disturb traditional business models in an economically ordered and safe manner.

This paper reviews the extant literature on the potential implementation of drones into the economic system and specifically how the implementation and ongoing use may be managed. Section 2 outlines our methodology for conducting the systematic review. Section 3 then presents our bibliometric results and discusses the issues being reported in the literature and highlights the four main use cases for drones (based on a content analysis of the reviewed papers) that we see being discussed. Section 4 examples current regulatory steps and then conclude in Section 5 with some discussion and identification of future research avenues, including the need for greater regulation of the drone ecosystem at the macro level and the potential for low altitude air management systems (LAAM).

2. Methodology

Originally developed in the medical literature the systematic literature review (SLR) has been used as a methodology in a range of management papers. In the transport literature it has been deployed in areas such as supply chain (e.g. Perera et al., 2018 ) and in aviation management (e.g. Ginieis et al., 2012 ; Spasojevic et al., 2018 ). Whilst not a strict laboratory controlled study ( Ginieis et al., 2012 ), they do give researchers and practitioners a flavour for the extent and coverage of the literature, and some vision as to where and by whom it is being generated and what it covers.

Drones have received literary attention for some time, primarily in legal/ethical, engineering and computer science fields. For this paper, we have focussed on management literature, given our interest in investigating drone management and related issues. Importantly, we ignore any military/defence use of drones to focus only on civilian applications. While ground-based and maritime drones are also present in the literature ( Pathak et al., 2019 ), the term ‘drones’ is now widely understood to refer to airborne ones, upon which we focus.

For our search, we developed a search string in Scopus composed of a keyword search for ‘drone*‘. We added synonyms like ‘unmanned aviation’, ‘unmanned aircraft*', ‘unmanned aerial vehicle*', ‘UAV’, or ‘remotely piloted aircraft*', which yielded 65,953 documents. We then restricted the results to the Scopus allocated subject areas of ‘Business, Management and Accounting’ (which includes a variety of areas such as innovation, strategy or logistics and supply chain management), or ‘Economics, Econometrics and Finance’ (yielding 1567 documents). Further, we restricted results to articles (published and in press), conference papers or book chapters (1133 documents), and we restricted the search to articles published in the last five years only, since the beginning of 2015 (519 documents). Finally, we limited results to the English language (505 documents).

Using Covidence (an online tool that aids in the faster review of documents through work flowing the review process and collaborative review), we analysed and filtered these articles. This was due to a variety of reasons. Initial screening results showed that for a substantial portion of the papers, drones are not the core focus of the paper and are merely an enabling device for the key topic of the paper, such as strategies for disseminating technology products into the construction sector ( Sepasgozar et al., 2018 ). Where drones were more significant, some articles were operationally (e.g. Zhou et al., 2018 ) or engineering focussed (e.g. Chen et al., 2017 ) with no substantial management consideration. Other articles were excluded as they were not relevant, including other uses for the word ‘drone/s’ (e.g. bees or employees) or UAVs (e.g. corporate finance terms). Articles without full text were also eliminated. Article content was further reviewed through Covidence, and the final sample of 133 articles was derived. Results were then analysed with Excel and Bibexcel ( Persson et al., 2009 ).

The identified papers are a population of different paper types. Some represent operational use case studies. Others are engineering focussed but are contemplative of future management endeavours. There are papers written from other (non-drone) perspectives that provide useful insight into drone deployment more generally. And in addition to bibliographic results, we found that use cases of drones to be a worthy area for discussion, as well as the current issues being experienced, which have expanded past historic issues to cover new ones that had not been encountered.

3.1. Bibliographic results

The following are selected results of our review. As illustrated in Fig. 1 , publications related to drone management (including case studies of their use) have been increasing.

Publication year.

Table 1 provides a summary of the published sources relates to our 133 reviewed drone papers. What is evident is that a few sources account for a significant number of publications on drone management in the investigation period. Also evident is a very long tail of single publication sources. What Table 1 also demonstrates is that drone management is still heavily domained in the technology and engineering literature. However, other types of journals are still present to cover specific drone issues (e.g. security and mining reclamation). As the management of drones appears to be very much about micro-level management instead of macro-level management, it is perhaps natural that technology, engineering and related literature are the major publication areas for drones to date.

Listed publication sources.

In terms of author contribution and potential thought leadership, there are 408 unique authors of the analysed papers, representing a wide and varied number of contributors. Of these, one has produced five publications (Hwang, J), one has made four publications (Liu, Y), three have made three publications ( Abaffy, L, Kim, H and Zhang, X ) and 16 have produced two publications. Aside from a number of author pair or group combinations in clearly linked publications from the same research activity, there does not appear to be any significant grouping/clustering of authors as is evident in other systematic reviews of other topics.

Similarly to authors, contributing institutions are wide and varied in range, with those with three or more contributions shown in Table 2 . Again, a long tail of institutional contribution is present, with some institutions having more concentrated contribution. Note that for these institutions, contribution may be planned but is more often unplanned, with different faculties (e.g. engineering and health) making independent, uncoordinated contributions to the literature. Inspection of the contributing departments reveals substantial contribution from engineering and computer science disciplines or institutes of that nature.

Institution contribution.

Country contribution is shown in Table 3 . Top 10 contributing countries and regions. The US, China, Australia and South Korea are significant contributors. Continentally (see Table 3 .), while Asia and North America are significant (to be expected based on the country results), the diverse efforts of European countries are also evident given Europe's substantial contribution.

Top 10 contributing countries and regions.

Our analysis of author keywords (543 keywords) revealed similarly wide and varied results, reflecting the wide range of contexts of research focus, as shown in Table 4 . Making allowance for similar keywords (e.g. drone delivery and drone-delivery), 442 unique keywords were identified. Excluding keywords used only once (386 keywords) and excluding 91 drone referential keywords that are not descriptive of an issue (e.g. drone, drones, UAV, UAVs, unmanned aerial vehicles), these keywords were identified multiple times. Key issues relating to privacy, security, acceptance and management are evident.

Keyword analysis.

We note that papers from earlier in the literature focus on conceptual issues such as privacy and security and have stood as a warning scene for industry to ensure that these concerns are addressed, and that policy makers will be alert to them. However, and concurrent with greater usage and chance to study this usage, papers later in the date range show a clear trend towards the consideration of more commercial aspects of drone adoption including how they are operated and used.

For example, the keyword ‘privacy’ appears in 2016 (four articles), 2017 (two articles) and 2019 (three articles). ‘Regulation’ appears in 2016 (2 articles), 2018 (one article) and 2019 (2 articles). The keyword ‘ethics’ appears in articles in 2016 (one article) and 2019 (two articles). However, ‘drone delivery’ is top of mind in the research community, quickly followed by how drones are going to navigate their way around. Of the drone delivery keywords, 13 of these (more than 80 percent) were published in 2019 indicating its rather recent focus in the literature, which is consistent with the drone use case discussion presented in section 3.3 .

3.2. Present and emerging issues in civilian drone usage results

In this section, we discuss some of the content of these papers. Operating in new spaces, in a third (vertical) dimension and proximity to other users, drone use is expected to have a significant impact on the quality of life, health, social and economic well-being ( Kyrkou et al., 2019 ). However, this potential disruption will, being a technological development ( Kwon et al., 2017 ), create issues and problems that require management to minimise negative impacts (as well as to maximise positive potential). Notably, however, our review indicates that these security, privacy and acceptance concerns, whist significant and relevant, are not as dominant as they have been in previous periods – with the use of drones in various ecosystems providing an opportunity for researchers to examine their introduction and impact on those with whom they interact.

Security management remains a critical issue. Invasion (intentional or not) of sensitive airspaces, like airports ( Boselli et al., 2017 ) and power stations ( Solodov et al., 2018 ) have the potential to and do cause costly disruption (e.g. the near-total closure of Gatwick Airport and disruption to fire and emergency services work in Tasmania in 2018). Safety is a perennial issue though automation may support improved physical safety outcomes ( Torens et al., 2018 ). Privacy issues remain a concern, particularly from drones that can capture imagery, particularly those that are used close to private personal space such as homes and apartments ( Daly, 2017 ; Aydin, 2019 ), or as drones are used in new ways, including research approaches ( Resnik and Elliott, 2018 ). Drone users, particularly recreational ones, do not have an understanding of the privacy requirements that they are subject to (Finn and Wright, 2016). Therefore, a regulatory response is likely to be required. Ethical issues around the use of drones for surveillance purposes are also present ( West and Bowman, 2016 ). Other amenity issues, such as the impact of noise, are also under consideration (e.g. Chang and Li, 2018 ).

The issue of drone acceptance therefore by the public remains an issue, though different parts of the community are more accepting than others ( Anania et al., 2019 ; Sakiyama et al., 2017 ; Rengarajan et al., 2017 ). Some literature (e.g. Boucher, 2016 ; Khan et al., 2019 ) notes that an outcome of this acceptance debate is that drones are being developed to be accepted, taking into account, instead of enforcing, acceptance of drones by the public, showing the role that ‘social license’ ( Gunningham et al., 2004 ) plays in the acceptance debate. Drones require societal trust ( Nelson and Gorichanaz, 2019 ). The demilitarisation of drones has facilitated trust ( Boucher, 2015 ), and positive media attention to non-controversial use cases has been shown to have had a positive impact on acceptance ( Freeman and Freeland, 2016 ).

The first stages of research into specific consumer reaction to drones have begun to bear fruit. Studies have shown how media positioning frames consumer and public responses to drone technology ( Tham et al., 2017 ). Recent work indicates that consumers may respond positively to drones. The technological aspects of drones have been identified to form a relationship with consumers through changing perceptions of risk, functional benefits and relational attributes ( Ramadan et al., 2017 ). Drones provide a psychological benefit to consumers and generate positive intentions to use drones ( Hwang et al., 2019a ). Perceptions of environmental benefits suggest favourable consumer perceptions of drone use ( Hwang et al., 2019b ). A study of motivated consumer innovations suggests that dimensions of functional, hedonic and social motivatedness are key drivers of attitudes towards consumption using drones ( Hwang et al., 2019c ). Innovativeness is noted as an attraction of drone food delivery services for consumers, with younger and female consumers more likely to be attracted by drones ( Hwang et al., 2019d ). Managing perceived risks associated with drone deliveries is a necessary task for foodservice delivery operators ( Hwang et al., 2019e ). In marketing, aerial drone photography is being well received by targets who respond positively to their inclusion in campaigns/advertisements given its cognitive stimulation ( Royo-Vela and Black, 2018 ). Use of drone imagery in this manner is, therefore expanding ( Stankov et al., 2019 ).

Operational management issues have begun to come to the fore with some studies beginning to examine drone maintenance regimes ( Martinetti et al., 2018 ), battery life management/charging and efficient performance characteristics ( Goss et al., 2017 ; Pinto et al., 2019 ). Importantly, with the move towards logistics, other questions are being raised, including how to optimise delivery strategies (e.g. El-Adle et al., 2019 ). Initial analysis indicates combined truck and drone delivery systems are a more efficient method of logistics delivery systems than current approaches ( Ferrandez et al., 2016 ; Chung, 2018 ; Carlsson and Song, 2017 ; Liu et al., 2018 ), Wang et al. (2019) . However serial delivery systems may be more efficient still ( Sharvarani et al., 2019b ) and overall delivery considerations need further analysis, such as preparation time for deliveries which are different between truck vs. drone delivery ( Swanson, 2019 ). Further research in different urban contexts may yield different results (e.g. dense urban areas with higher density and shorter trip distances). Take-off and landing management processes (Gupta et al., 2019; Papa, 2018a , 2018b ; Papa, 2018a ) and ground handling operations ( Meincke et al., 2018 ) are also evident in the literature. Using longer-range drones for civilian purposes is beginning to be discussed (more so of remotely piloted drones instead of automated ones) ( Tatham et al., 2017a ) and the development of specific, commercial drone aviation parks for large drones has been completed ( Abaffy, 2015a , Abaffy, 2015b ).

Initial strategic impacts are receiving literary attention. Drones are driving entrepreneurial activity ( Giones and Brem, 2017 ). Magistretti and Dell’Era (2019) show that operators use four main types of technology development strategies when using drones: focus (adding drones to current operations), depth (expanding current operations more fully), breadth (expanding operations across new offerings) and holistic (developing wholly new operations or approaches). Both Kim et al. (2016) and Meunier and Bellais (2019) note that drone technology leads to spillover effects in other sectors. Hypothecations of societal impacts of future drone issues are also being made ( Rao et al., 2016 ). Consideration of their use in extra-terrestrial environments is also contemplated ( Pergola and Cipolla, 2016 ; Roma, 2017 ).

In the next section, we analyse drone use through several revealed use cases.

3.3. Primary use cases

A valuable part of our review and a key finding is our contribution to understanding how drones are deployed. A large proportion of reviewed articles are (usage) case studies rather than a systematic analysis of an issue. Through these papers, we can highlight that there are presently four primary categories: monitoring/inspection and data acquisition, photography, logistics (including passenger), and recreation. Even accounting for the lag between events and their academic publication, we view that the categories below are reflective of unpublished but current use types.

3.3.1. Monitoring, inspection and data collection

With lower capital costs and greater capabilities, drones can capture existing data in new ways, or capture uncollected data for new analysis. Industrial users are taking advantage of the new opportunities being offered by the technology to do things in new ways, for the same or better outcome.

Network management businesses, e.g. pipelines or energy transmission ( Li et al., 2018 ), road maintenance ( Abaffy, 2015a , Abaffy, 2015b ) and railway operation ( Vong et al., 2018 ) have swapped costly inspection teams with drones. Some inspection drones have real-time analysis capability and quickly report issues and objects for investigation back to the base rather than involving separate analysis stages. These users mainly deploy drones on their specific network geographies (within a set meterage from the network line) however, in positioning to and from their inspection areas, they may traverse open airspace. These network geographies are often in public spaces and given that powerlines (and sometimes rail/road networks) are placed over private properties via easements, management of drone airspace use is important.

Agricultural (and related) industries are inquisitive when it comes to learning more about the land they manage and naturally have looked at drone technology to capture new information ( Weersink et al., 2018 ). Farming has had a recent history of using satellite information to identify crop health issues, using data collected to more efficiently target the application of fertilisers and pesticides. More recently, drones have acquired this information ( Na et al., 2017 ). This has financial implications, but also environmental impacts, as reduced inputs lead to reduced negative impacts for the same output. Similarly, mining operations have used drones to remotely manage and optimise different elements of their production process ( Wendland and Boxnick, 2017 ), including monitoring stockpiles of ore and leeching pads for maintenance issues and analysing blast ore before its processing ( Bamford et al., 2017 ), accessing waterbodies in hazardous/remote locations to facilitate sampling for environmental management ( Banerjee et al., 2018 ; Langhammer et al., 2018 ) and imaging mines for rehabilitation ( Moudry et al., 2019 ). The construction industry uses drones in planning construction sites cheaper than other means (such as helicopters) and at lower risk to staff ( Abaffy and Sawyer, 2016 ; Li and Liu, 2019 ) and hazardous industrial plants use drones to monitor gas production ( Kovacs et al., 2019 ). Importantly for all of these industries, use of drones takes place largely in the airspace above the mining or farming areas and may have minimal impact on other users (notwithstanding that mining and farming areas are generally quite distant from urban areas).

Drones are also used by government and regulatory agencies for surveillance purposes and to monitor compliance. The technology has, for instance, been used in New South Wales to monitor land clearing, both to ensure that permits are complied with and to check if illegal land clearing has taken place. In hard to access areas, air pollution monitoring has been undertaken with drones ( Alvear et al., 2017 ). Drones were used to assess urban damage in the aftermath of floods, hurricanes and even the 2011 Fukushima nuclear reactor disaster ( Hultquist et al., 2017 ). Drones are also used to assess compliance with rehabilitation performance ( Johansen et al., 2019 ), and just this year have seen use in shark monitoring trials at beaches. Emergency services are making more use of drone technology. While some of this use has overlap with logistics (refer below), using drones in search and rescue is a logical move to increase the capability of rescue activities ( Lygouras et al., 2017 ; Kamlofsky et al., 2018 ). Despite the disruptive potential noted above, the monitoring use of drones is useful to fire management ( Athanasis et al., 2019 ) and surf lifesaving ( Lygouras et al., 2017 ) teams. Drones see use in humanitarian relief uses ( Bravo et al., 2019 ; Carli et al., 2019 ). The use of drones for security monitoring is also increasing ( Anania et al., 2018 ; Sakiyama et al., 2017 ). Sensitive but large area enterprises such as forestry or solar cell farms can monitor and inspect remotely with drones ( Xi et al., 2018 ; Saadat and Sharif, 2017 ). These uses are often performed over public and private property and therefore impact a range of other users. However, they are also supported by regulatory requirements and are often undertaken for public purposes and so might be more accepted by the general public.

3.3.2. Photography/image collection

Photography is another special form of data acquisition. While monitoring/inspection uses by industry might also use photographic means to acquire data, this is to convert visual imagery into data to support decision making. However using photos solely for aesthetic value has become an important use of drones in its own right, mainly for personal use (such as the documentation of a person's special event), but also increasingly for commercial use such as sporting events or in marketing campaigns (e.g. Royo-Vela and Black, 2018 ; Stankov et al., 2019 ). Being able to fly has been a dream of (some) humans since time immemorial, and use of drones to capture imagery from birds-eye-views is attracting substantial interest from some quarters.

Use of drones for this purpose is somewhat ad-hoc, and in a large number of cases involves the use of public space as users document their weddings, family events, naturescapes or other events (either themselves or through a commercial operator). However some uses (e.g. farmers taking drone photography of their farm operations) take place entirely over the privately owned property of the drone operator, and some of the aforementioned events happen over publicly but remote land that is not intensively used like urban public land. For sporting events, such as football matches, golf tournaments and car races, use is largely confined to space above the event and closely managed by the event manager to maximise the photographic potential of the event and avoid event disruption.

3.3.3. Recreation

Drones as recreation is a new use, though mimics things like remote-controlled cars which have provided people with entertainment for many decades. The explosion of recreative use shows how popular the phenomenon is, as people take advantage of the third dimension for leisure, which for a long time has been a luxury only enjoyed by those who could fly (in various forms) or partake in risky sports. Drones are being used, e.g. in tourism activities ( Song and Ko, 2017 ), and there are even competitive drone racing tournaments ( Barin et al., 2017 ). Drones are also being used as three-dimensional art installations to generate linked visual structures with no other purpose than entertainment ( China Global Television Network, 2019 ).

The expansion into recreative space is perhaps linked to the increasing acceptance of drone technology by the public as people become more familiar with the technology and begin considering their potential uses for it. Most recreative use is over public spaces such as parks and other such spaces with some of it in non-urban areas being conducted over farmland and naturescapes (either owned or not by the drone operator), though is limited by the low complexity of drones available to use for this purpose.

3.3.4. Logistics

Perhaps most interesting, and most in need of management consideration is using drones for logistics purposes. In its very early days, this use case has perhaps the most significant potential for disruption. Current discussion contemplates that their use will enhance supply chain efficiency and effectiveness ( Druehl et al., 2018 ). Indeed, currently inside warehouses, logistics firms are using drones to manage inventories ( Xu et al., 2018 ). Externally, drones have been used for medical supplies ( Prasad et al., 2018 ; Tatham et al., 2017b ) and organ deliveries ( Balakrishnan et al., 2016 ) in different contexts so far, but with trials for aerial pesticide application ( Zheng et al., 2019 ) and food deliveries currently underway, their use in broader delivery services (e.g. Drone-as-a-Service Asma et al., 2017 , Kang and Jeon, 2016 Shahzaad et al., 2019 ) may lead to substantial shifts in delivery service execution. Prospective applications also include postage/package delivery, with interest being shown by major logistics firms ( Connolly, 2016 ) and the potential for other drone facilitated household services (e.g. dry-cleaning collection/delivery). But we are sure that this is just the tip of the iceberg of opportunity for drones in the logistics space. Indeed, the potential for personal logistics (i.e. humans) is also a goal of some operators ( Lee et al., 2019 ) which would call for significant regulatory oversight (especially safety). Large scale industrial applications are also being investigated ( Damiani et al., 2015 ). The list of potential uses is extensive, and the development of drones in this way is likely to be revolutionary however initial findings are suggesting that they may only be feasible in congested urban areas ( Yoo and Chankov, 2018 ).

The above use classes show the wide spectrum on which drones are used. Clearly, both the literature and observations of trends outside the literature show that these uses will expand. Several questions in many contexts are open for academic exploration at this time, and a few that are of interest to us we will present here (though our specific areas for further research for our paper topic we will discuss at the end of this paper). In the future logistics space, an important question we believe will arise is who owns drone fleets? Will drones be owned by individuals (e.g. mobile phones and private cars) or will they be owned by fleet management/delivery companies and used in an on-demand manner (as common in traditional wet leased air freight operations; e.g. Merkert et al., 2017 )? A drone premium is likely to be chargeable given the convenience and time-saving factors but who will ultimately pay this premium? Will it be added to the delivery cost of goods and services (as in the current postage cost model) or will goods providers decide to use drones for competitive advantage and absorb the cost as part of their cost structure (offsetting delivery cost savings)?

But the key question on our minds for the remainder of this paper is the management of the significant volume of traffic that these movements will create. Increased and increasing use will be more invasive of airspace than current usage, which if not managed appropriately, and if not managed for community standards (within the license to operate), may lead to rejection of the technology and the benefits that they are purported to bring.

4. Managing the drone revolution – current regulatory approaches

We have alluded to the specific issues that drones will present above. Solodov et al. (2018) describe a range of particular drone threats, in the forms of surveillance, smuggling, kinetic (i.e. collision), electronic and distraction. Solutions to these threats include both non-destructive means (such as software intervention, UAV vs UAV, ground-based capture/interference and bird-based methods) and destructive (including electromagnetism, lasers, firearms, and missiles ( Solodov et al., 2018 ). Some airports are working to manage drones in their airspace (e.g. Sichko, 2019 ; Mackie and Lawrence, 2019 ). Many of these methods are reactive or defensive. Instead, more proactive and preventative methods of management would be warranted. Current regulatory approaches are looking to assign responsibility to the operator, which is, in reality, a concern for both consumer and operator ( Liu and Chen, 2019 ).

But further management of lower airspace is a growing area of policy consideration. Across the globe, laws and regulations will need to be created to manage drone impacts. Jurisdictions across the world are examining the drone use and building regulatory environments around them. Chen (2016) identified that the legal and regulatory framework in the US needs reform to facilitate commercial purposes. Integration of drones into the presently regulated airspace (particularly in urban areas and areas of higher sensitivity) is seen by industry to be a likely policy outcome ( Torens et al., 2018 ). Various consistent jurisdictional approaches to this regulation are under development, some of which appear consistent with that envisaged by Clarke (2016) , and the European approach is said to focus on the operation of the flight, rather than the aircraft itself ( Hirling and Holzapfel, 2017 ). This might be described as an approach to softly regulate the industry as it presently stands to allow for safe participation. These regulatory measures significantly increase the requirements of operators to build cultures of safety into their operations. This approach bears a resemblance to other transport sectors (i.e. non-drone aviation, railways and road vehicle operations) which require pilot/driver licensing and firm accreditation. Regulators worldwide are looking to manage the drone itself (weight and size) who flies the drone (both organisationally and personally), how they fly it (height, day/night, speed, visual line of sight), where they fly it (restricted areas, near people, near private space) and other factors (such as the number of simultaneous drones operated) ( Civil Aviation Safety Authority, 2019 ).

The approach by regulators in most jurisdictions so far to grow regulation with the industry, instead of trying to foresee the future and regulate that, is one that may (and are indeed intended to) be designed to support entrepreneurship, innovation and economic growth ( Chisholm, 2018 ).

However, despite the above, it is clear that even in jurisdictions that are well advanced in terms of established drone governance frameworks, more regulation will no doubt be required. The above framework does not cover the full regulatory gap between current drone use and the non-drone airspace. Operators seeking to operate outside of the limits of the above regulation will arise and require further management. Drone automation will mean that pilot intervention to manage the drone in the event of abnormal operations will be impossible. However, there will remain human-controlled drones (including remote ones) such as for recreation or ad-hoc, customised usage. Manned and unmanned drones will have to operate together, and both modes will involve new levels of complexity, particularly as drone numbers increase. Questions will arise about how to manage drones across the industry, where individual adoption by firms will more than likely require harmonised regulation to support supply chain efficiency ( Druehl et al., 2018 ; Foina et al., 2015 ). And different operators will run subnetworks with different path optimisation plans ( Liu et al., 2019 ; Jeong et al., 2019 ). With the substantial increase in flying, in both time and frequency terms in particular, drones are going to have a far more significant impact than the current regulatory impact can manage.

5. Managing the drone revolution – where to from here?

Given the relatively low level of literary consideration, the opportunities for interesting research into the control and macro-management of drones are significant, wide and varied. However, in the context of this paper, the primary area for further research that we see as relevant is how the new drone management ecosystem is to be managed in the macro sense. There are still a raft of challenges to be overcome ( Zhou et al., 2018 ), however with the prospect that drone flight will be as normal as car trips, and that they will play a role in ‘smart’ cities ( Mohamed et al., 2018 ), how to ensure that this new system is not only safe but also productive is essential.

An Internet-of-Drones ( Edwin et al., 2019 ) is a very potential future. Research into the use of flying ad-hoc networks to monitor and manage deviant drone behaviour ( Bahloul et al., 2017 ; Barka et al., 2018 , Karthikeyan and Vadivel, 2019) are in progress, as are geofencing ( Boselli et al., 2017 ) and signal jamming ( Chowdhury et al., 2017 ) that act on the navigation systems within drones to prevent drone incursion into restricted areas. Though to implement some of these preventative technologies, it is, of course, necessary that the relevant drones have navigation technologies installed to be acted upon by the countermeasures, which for a substantial number of retail drones is not the case. For those that do have navigation technology, research efforts are quite extensive into developing algorithms and programs to facilitate orderly inter-drone coordination like network registration processes ( Agron et al., 2019 ) incorporating obstacle detection, ( Zheng et al., 2016 ; Zhu et al., 2017 ; Choutri et al., 2019 ; Abdullah et al., 2019 ), separation processes and collision avoidance ( Tan et al., 2017 ; Nysetvold and Salmon, 2019 ) the impact of weather on drone performance ( Vural et al., 2019 ), completion of common tasks ( Zhuravska et al., 2018 ; Abraham et al., 2019 ; Fesenko et al., 2019 ; Zhu and Wen, 2019 ), inter-drone information security ( Abughalwa and Hasna, 2019 ) and operation in GPS poor areas ( Siva and Poellabauer, 2019 ), though many of these are conceptual and theoretical deployment (e.g. Kim and Kang, 2019 ). Connecting independent networks of drones (that are expected to exist in the future) is yet to appear in the literature, though some elements of this are developing such as using drones as nodes of a multi-drone communication network ( Kuleshov et al., 2018 ; Smith et al., 2018 ; Xiao and Guo, 2019 ). Though note, these methods are only for local drone coordination of the drone and static obstacles (e.g. buildings) or a few connected drones or drone micro-management – systems and processes being developed to impact the drone from the drone's perspective. However, more thinking about drone macro-management and their broader interaction with the environment needs progression, particularly how to manage drones and their collective impact on the remainder of society so that this impact is positive.

Industry is turning towards this question with operators looking to develop more complex management systems. It is likely that (as for aviation generally) each operator will look to develop a customised way of managing drones to suit their operations, such as for search and rescue systems ( Mohsin et al., 2016 ; Mondal et al., 2018 ), complex distribution networks ( Shavarani, 2019 ) or routings with ad hoc targets ( Suteris et al., 2018 ) which will no doubt be complex given the use of the third dimension ( Pandey et al., 2018 ). The concept of an overarching coordinating network is gaining traction in industry and government - NASA is, for instance, looking to integrate UAS into the national airspace system ( Luxhøj et al., 2017 ; Matus and Hedblom, 2018 ; He et al., 2019 ). Conversely, the industry has a different view. Logistics and technology firms such as Amazon and Google are looking at using drones in their parcel delivery systems and firms such as Uber are looking to introduce point to point passenger drone services. Small scale trials are underway in various locations globally, where industry is developing their navigation systems to manage drone delivery. Industry is making the argument that they would be able to self-regulate their drones with these systems, designing them to communicate between drones of different operators and centralised processors. These systems would look to simultaneously program the most efficient routes for deliveries (taking into account, mitigate and avoid collisions and incursions that may cause damage not only to other drones but also to other non-involved parties).

A competing view is considering whether drones should be integrated into the overall air transport management system ( Zhang et al., 2018 ) and managed using many of the same tools and mechanisms deployed by regular aircraft such as identification and collision avoidance systems ( Lin, 2019 ). There is a view that far more oversight of the sector will be required to ensure that safety conditions can be met, and that airborne drones cannot operate separately to large aircraft with which drones will share airspace. A system through which this control can be exercised is being called by airspace management technology developers ‘low altitude airspace management systems (LAAM)’. LAAM as currently envisaged may replicate the control mechanisms used for general and civil aviation flights. Still, importantly each of these different types of flights, drone and non-drone, will know about all other flights in making flight planning and execution decisions. They will be able to communicate with drones and record their position and use within the network. Other features might also be incorporated into LAAM, including the ability to issue instructions to drones (for say crash avoidance) or the ability to enforce geofencing boundaries to prevent drone incursion into specified issues. They may be able to aid in congestion management, to ensure that all drones can achieve their missions within reasonable parameters and may include mechanisms to facilitate flight planning and operations, consistent with current air and rail control management systems. Real-time management of issues would be an essential feature of LAAMS ( Zheng et al., 2016 ). To us, the debate over centralised or distributed airspace management is quite interesting, not only for the impact that it may have on airspace management for drones but also the precedents it may make for other sectors. The impact of such coordination systems on public drone acceptance would also be of interest for researchers to address considering the involvement of government in such regulation may be trusted more than that of the private sector.

From an engineering and technical perspective, the areas of research that are required are almost endless, as new systems are scoped, designed and developed to integrate within the current regulatory environment and aviation control systems. But from our perspective, that of management, there are a few key areas of research that stem from the question of LAAM implementation. Firstly, the need for LAAM and what they are to do needs better articulation from those who would be impacted by it.

As noted, key potential future users of such systems are discussing their need, but further consultation is required to detail precisely what is needed. There are significant policy and commercial/regulatory discussions, but from an academic perspective, this discussion will provide useful insight into a range of issues. An immediate area to investigate are the perspectives of current recreational and commercial users and their reaction to such a possible integration into LAAM and determining what they may like to see for themselves if LAAM is implemented. Current regulations enforce rules on operators which may not be required with a LAAM. Besides, research into prospective users and their preliminary strategies, pricing and other decisions that firm such as logistics ones will make when using the network. Consideration of overall supply chains and the changes that drones may bring in the context of LAAM, helping to not only enable but cheapen the use of drones and impact a range of upstream and downstream elements. Retail precincts may be impacted by yet more package delivery. Warehouses may look quite different from what they do now. Drones may replace hydrocarbon fuel consumption with electric fuel consumption. They may also remove trucks from roads, particularly urban delivery ones. And individual supply chains and travel patterns may change as drones become part of everyday life.

Other transport management specific questions remain to be answered, as highlighted in the literature. Delivery substitution decisions will also be of interest to academia. Cost will be a driver of these changes, but other factors such as service quality and the types of services offered will become a focus area. Optimal drone network designs will be an interesting avenue of discussion (e.g. Pulver and Wei, 2018 ) which will vary depending on the purpose of the drones employed. Optimising how truck and drone fleets interact may be a useful transitive measure to help improve delivery time and efficiency ( Freitas and Penna, 2018 ). Consideration of other delivery mechanisms is also worth researching, such as replacing the truck with a parent drone ( Kim and Awwad, 2017 ). Medical deliveries will need higher prioritisation on the network to ensure their rapid delivery from the donors to the operating theatres where they are needed, or transit points through which they will need to travel ( Balakrishnan et al., 2016 ). So some form of prioritisation matrix will be required.

A key limitation of our approach and any literature review more principally is the lack of full comprehensiveness as literature in the relevant subject area is a proliferating (past the cut-off date and the publication of the paper) and b) not confined to academic outputs (i.e. those indexed in SCOPUS). During our grey literature review, we noticed a recent surge of consultancy reports on drone use cases in the context of urban air mobility (UAM) as a new mode of transportation (e.g. Baur et al., 2018 (Roland Berger); Booz Allen Hamilton, 2018 ; Grandl et al., 2018 (Porsche Consulting); Thomsen, M., 2017 (Airbus)) which suggests that academic papers covering this topic will follow. Indeed, Fu et al. (2019) is a first in a potential series of such papers and has been included in our review.

In summary, our literature review results suggest that security, privacy and acceptance concerns, whist significant and relevant, are not as dominant as they have been in previous periods – with the use of drones in various ecosystems providing an opportunity for researchers to examine their introduction and impact on those with whom they interact. We conclude that further work is needed to understand potential impacts of drone usage (e.g. fatalities due to accidents), subsequent potential risk trade-offs and adjustment/formulation of new regulation ( Hirling and Holzapfel, 2017 ), The safety/cost trade-off will be an important one to contribute to the setting of appropriate safety rules that facilitate the industry without constraining it unnecessarily, including the development of low altitude airspace management systems to support the increased deployment.

Acknowledgements

We acknowledge the contribution and comments received from participants at the 2019 Air Transport Research Society 23rd World Conference. The comments from two anonymous reviewers have helped us to further improve the paper for which we are thankful. We are grateful for the comments and financial support received from Thales Australia and the University of Sydney Business School through an Industry Partnership Grant.

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A review on drones controlled in real-time

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

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

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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.

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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.

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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.

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

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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

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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

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• Unmanned areal vehicles
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• Global positioning system
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