1. Introduction
Uncrewed flight using Remotely Piloted Aircraft Systems (RPAS), commonly referred to as drones, has long been used in aviation operations. Civilian use of drones has accelerated in the past four decades, driven by technological advancements, resulting in a reduction in the capital and operating costs of uncrewed flight. Drones are now readily available to both recreational and commercial users facilitated by the continuing development of the human–drone interface, making it easier for non-expert users to operate a drone with minimal training [1]. This has resulted in newcomers flying drones in airspace who have never been part of the aviation system. Bartsch [2] describes this situation as the “4A’s”, accessible, affordable, adaptable, and anonymous. With this rapid proliferation of drones, there has been a lag in both the knowledge, skills, and other attributes (KSaOs) required for safe drone operations and appropriate regulation as international and national regulatory authorities struggle to implement rules that are specific to drone operations. Further, there has been an attitude within some national regulators of uncrewed flight not being part of the aviation system as Albihn [3] illustrates from the Swedish setting, where drones were not thought of as a natural development in aviation but rather a “a parallel subindustry that quickly evolved from its military/toy roots” (p. 7).
The licensing of conventionally crewed pilots has been tightly controlled since World War II with international conventions and agreements. To date, this has not fully happened with unmanned flight operations. Efforts to include these new aviators within the existing structures and culture have been made using existing competencies for conventionally crewed flights. However, there are marked differences between conventionally crewed flight and uncrewed flight such as the lack of direct spatial perception for the pilot/operator, the lack of direct cues such as noise or vibration, and a lack of direct control of the system, leaving the system open to distortions, disruptions, and break downs in the connectivity links. These differences have resulted in a lack of understanding of the separate competencies, knowledge, skills, and other attributes (KSaOs) required for drone operators.
The many studies on unmanned KSaOs have primarily ignored the human side of Remotely Piloted Aircraft (RPA) operations. They have emphasized the technological side of the RPA equation such that Herz [4] believes that “many strategists tend to underplay the role of human factors and oversell the technology to benefit their company’s product” (p. 43). Stark et al. [5] also note there have been many human-based articles within the RPA domain, but many of these articles are focused on robotics. Challenges for identifying the required KSaOs of RPA operations include the range of uses that a drone can be applied to. Herdel et al.’s [6] review of the literature identifies 16 domains that use drones, from which come 100 applications. There is also the challenge arising from the range of size of the vehicles, from micro-drones to those as large as transport aircraft [7], that can be used for operations. A further challenge is the role of the human in the Remotely Piloted Aircraft System, which changes depending on the task allocated to the drone and the degree of autonomy for the drone while completing the task. Tezza and Andujar [1] break this down as the human being an active controller of the drone as a recipient of the drone’s activity, having social interaction with the drone, or being a supervisor of the autonomy (p. 167440). The ISASI [8] identifies the difficulties in being able to describe the requirements for who is competent to operate a RPA because there is “insufficient objective research available to allow air safety investigators to make any judgements regarding how an unmanned aircraft system pilot should be trained or certified.” (p. 16).
2. RPAS Regulations
ICAO first published guidance material for drone flying in 2006. This was a departure from the SARPs issued by ICAO for conventionally crewed flights [2]. Following the establishment of a working group to produce appropriate drone regulations, the Unmanned Aircraft Systems Study Group (UASSG), the Remotely Piloted Aircraft Systems Manual (RPASM) was produced in 2013. This was again only guidance material for national bodies. In 2020, at the bequest of member states, ICAO promulgated ICAO Model UAS Regulations Parts 101, 102 and 149 and the associated Advisory Circulars (ACs) that could be used by national bodies to form their own RPA regulations. These model regulations do not require “the operator to obtain a certification of competency” [9], but there is an expectation that RPA operators will act in a safe manner and be aware of the airspace the operation is undertaken within.
The outcome of having only guidance material has been a lack of uniformity and a resulting diversity in the national regulations for drone operations across all jurisdictions in direct contrast with conventional flight operation regulations [10]. MacPherson [11] identifies the variances in drone rules in the different jurisdictions of the United States, Singapore, and New Zealand, including the differing requirements for the licensing of drone operators. Hodgkinson and Johnston [12] note that the FAA did not accept Remote Pilot Licenses (RePLs) from other national regulators because of the lack of developed standards for RPA operators.
Australia has been one of the leading countries in introducing regulations for drone operations, including the licensing of organizations and individual operators. Part 101 of the CASA Rules was first promulgated in 2002. In 2016, CASA introduced new rules for the operation of RPAS with changes in Part 101. The goal was to make the rules surrounding RPAS operations simpler and easier for those operations deemed to be low risk. Part of the rewritten rules was the introduction of new weight classifications, including the sub 2 kg UAV described as the very small class of drone, which was now considered to be excluded RPA. There was no longer the requirement to meet as many of the regulatory requirements as were previously needed. For those operating this class of RPA, they did not require certification (ReOC) for the company or licensing (RePL) for the RPAS pilot.
However, these changes to Part 101 were not universally accepted. Part of the concern was the lack of training for those RPAS operators who wanted to operate commercially and the threat these poorly trained operators posed to other aircraft and people on the ground not associated with the RPA operation. The result was Part 101 being amended in 2019. These changes saw a tightening of the requirements for operators of drones weighing less than 2 kg, as they need to complete an online quiz, and operators of drones heavier than 2 kg need to obtain a license to operate the drone, a RePL [13]. The current syllabus for the Part 101 RPAS license syllabus is prescriptive, with the students required to have knowledge of the unmanned aircraft and its systems, navigation, law, aerodynamics, and human factors.
With the many different variables such as range of tasks, range of aircraft size, national variations, and the guidance-only publications from ICAO, there remains the need to continue to explore further the KSaOs that are needed to be utilized by skilled, knowledgeable drone operators completing different tasks to produce the safest outcomes without stifling the productive outputs and future potentials of drone operations.
3. Review of RPAS Competencies and KSaOs
The many different studies on what is competency can be classified into three broad areas or perspectives, which arise from different disciplines. Van Loo and Semeijn [14] describe these perspectives as the educational perspective, the labor market perspective, and the human resources perspective.
The educational perspective views competencies as a composite of knowledge, skills, and attitudes. It is closely linked to meeting the requirements of jobs within the marketplace. It can sometimes not meet the competencies and demands of yet-known situations people may encounter.
The second perspective is the labor market understanding of competencies, which Van Loo and Semeijn [14] identify as being closely related to skill and qualifications.
The human resources perspective is the third perspective and seeks to understand competencies as more than qualifications but rather as the “total spectrum of human behavior” [14], which is used for the benefit of the organization. Not only is there an inclusion of the knowledge, skills, and attitudes but it also involves a more social meaning.
Jeffrey and Brunton [15] identify three perspectives or views on competency found in the literature, described as individual characteristics (found in the human resource development literature), and those required for organizational performance (management literature) and the aligning of education outcomes with job requirements (vocational and training literature).
Flin [16] identifies the competencies required for a safety critical activity (such as RPA operation) as being like the general competencies required in most occupations and professions, including “collecting evidence, setting out performance criteria, having standards, documenting key knowledge and skills (including non-technical) and the use of independent, credible and competent assessors” (p. 7). The aviation sector has, over the years, developed systems that not only have attempted to define the competencies required for licensing but have a rigorous follow-up system of checking the maintenance of those competencies with annual or even twice-yearly checks.
There have been many efforts to identify the specific competencies required for the safe operation of RPA, utilizing differing perspectives on competency that focus on the first perspective of the knowledge, skills, and other attributes (KSaOs) required for the job.
Torrance et al. [17] define knowledge as “information that is acquired through formal and informal learning” (p. A 1). Skill is defined as “the capability to perform job tasks and is developed through training and/or practice” (p. B 1). They differentiate between skills and abilities with the latter defined as “a general human trait possessed by an individual that gives them the capacity to complete mental and physical tasks required of the job. Abilities are innate rather than learned attributes.” (p. C 1). Other attributes are defined as “an attitude, preference, or personality trait that influences the extent to which an individual can complete job tasks. Other characteristics include innate traits and learned preferences.” (p. D 1).
Studies on unmanned flight KSaOs stretch back nearly fifty years when Kiggans (1975) [18] explored the possible criteria for RPA operators. Howse [19] identifies one of the first studies being conducted in 1979 by the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI). Although there was early research into the topic, there appears to be a 19-year gap before further studies were published. At the turn of the century, Weeks [20] was recommending further “research into the essential skills of UAV operators to introduce an empirical frame of reference for evaluating qualifications.” (p. v).
The early research was connected to military drone operations [21]. The conditions under which military RPA operations take place may not often be applicable to civilian users of RPAS. As an example, Schmidt et al. [22] identify some of the differing challenges between military and civilian RPA flying as including military working in teams and flying predominantly fixed-wing RPA. Civilians fly mainly single-pilot multi-copter drones. Therefore, it is unknown whether the required KSaOs for military RPA operations are transferable to civilian RPA operations.
With the still immature RPA sector, the continuing research on the KSaOs of RPA pilots draws on the experiences of existing professionals in the sector [23].
A review by Torrance et al. (2021) [17] finds 88 articles that are relevant to the identifying of KSaOs of RPA pilots. From this, they identify 37 knowledge competencies listed in the articles, 38 skill competencies, and 39 other attributes.
Schmidt et al. [22] seek to use existing knowledge and the experience of civilian RPA pilots to identify required KSaOs. Conducting focus groups with these pilots, they develop a list of 104 competencies that are identified as being required for the task of flying a remotely controlled aircraft. These involve both generic airmanship competencies that are pertinent to all sectors of aviation operations and competencies unique or specific to RPA operations. This list is distilled down to the 38 competencies considered most important by the focus group members, comprising 11 flight skills competencies, 10 knowledge-related competences, 6 cognitive ability competencies, 5 interpersonal skills competencies, and 6 personality aspect competencies. A questionnaire is formulated from these 38 competencies and sent to professional civilian RPA pilots in Germany. The goal of the research is to identify the training needs of RPA pilots, so the competencies identified from the responses are placed into one of three levels of training requirements, no training required, training required, and refresher training (described as overtraining by the researchers), depending on the response of the pilots to the frequency of use of the competency, its difficulty, and its criticality.
There is only one competency identified as not requiring training, and that is the use of checklists. Within the training category, 16 of the 38 competencies are identified by the respondents as being required. One is flight skill competency, five are cognitive ability competencies, two are personality aspect competencies, and two are interpersonal skill competencies of communication skills. The importance of communication skills is also noted by Bantugan [24] as a key competency for RPA operations.
In the refresher training (overtraining) category, 21 of the 38 competencies are identified by the respondents as being required. There are three knowledge competencies dealing with performance limitations and emergency operations, one with cognitive ability competence, four with personality aspect competencies, three with interpersonal competencies, and ten flight skill competencies.
While it may seem somewhat counterintuitive that more flight skill competencies are categorized for refresher training, these flight skill competencies are for advanced flying skills such as flying in close proximity to obstacles such as buildings and non-normal procedures such as flying without GPS. Schmidt et al. [22] conclude that initial training only is insufficient. There is a requirement to continually train for higher skill operations and emergency procedures that the RPA operator will rarely (hopefully) have to conduct.
Ljungblad et al. [23] use the methodology of interviewing a diverse cohort of professional RPA pilots across a range of nationalities to describe the important priorities and the way in which professional drone pilots conduct their operations. They find that the contribution of the drone pilot to the task of operating a drone is not primarily flight skills but using the drone to assist in the job. The drone is not an aircraft but a “tool of the trade” to help the operator complete their professional task. For most respondents, drone flying is just one of the tasks they have to undertake and is often secondary to other professional tasks.
From the interviews, the researchers note there is a range of material needed to be learnt during training for drone operations, including flying skills, aviation knowledge, and aircraft system knowledge including the operating software and data collection methods. These are generic KSaOs required across all operations. There are also specific competencies for the completion of the professional tasks. Cloaking these generic and specific KSaOs is safety, which is paramount for all the professions taking part in the interviews.
McKinley et al. [25] search for similarities between pilots and video game players to ascertain if any competencies developed playing video games could be transferrable to the RPA operations context. Their findings indicate there are certain skills developed by video gamers that do have transference and could be applied to RPA operations as universal competencies.
Pilot personality is also debated as a KSaO, which aligns with the definition of “abilities” by Torrence et al. [17]. While Rose et al. [26] find that pilot personality could predict training outcomes, Schwab’s [27] response would indicate that the promised prediction scores of aspects of personality are not as strong as Rose et al. [26] claim.
Dalilian and Nembhard [28] use eye-tracking to measure behavioral and biometric markers as students acquire drone piloting skills on a drone simulator in a controlled setting. For different tasks at different levels of difficulty, experience gained through practice contributes to better performance.
The KSaOs listed in the various studies can be contradictory. For example, Tvaryanas et al. [29] identifies that previous manned flight experience is not a requirement for RPAS operation; in fact, it could even be a hindrance. Those experienced manned flight pilots transferring to RPA operation may have to unlearn flying skills to be successful in the new domain. However, countering that is Schwab’s [27] description of German Air Force RPA operators coming from within the ranks of manned pilots who hold a license and an instrument rating. Manned flight experience is, in this instance, a requirement for RPA training.
In summarizing the literature, there is a growing body of understanding of the required KSaOs of RPA pilots that are both similar and at variance with conventionally crewed flight. However, there is still some distance to go to have a full understanding of what is required to operate RPAS in a safe manner.
4. Digital Badges
The KSaOs identified by the research for safe RPA operations are often generic, becoming the basis for licensing requirements. With the wide range of operations that drones are being utilized for, there is a need to understand further the unique and specific requirements for each type of operation; for example, those required for agriculture flying would not be needed for building inspection flying. As Ljungblad et al. [23] identify, the flying of the drone is only a small part of the operation, and the professional task is often larger. The uniqueness of these KSaOs can make it difficult to incorporate them into licensing requirements. Micro-credentialing and digital badges can offer a solution by providing tangible evidence of the competence of an operator in specific RPA KSaOs. The generic competencies would be covered by the issuance of a remote pilot license or certificate, and the specific competencies would be covered by micro-credentials and indicated by a digital badge. This would ensure RPAS operators not only have a foundational license to operate a drone but also the specific KSaOs to conduct a unique operation.
There has been a growing interest in micro-credentials and digital badges, a genre of credentialing [30], where credentials can be described as a qualification that can be taken to the marketplace for the purpose of securing employment. In Australia, the government, through the Universities Accord, is looking to have universities develop micro-credentials and digital badges.
Digital badges are defined by Fanfarelli and McDaniel [31] as “visible markers of achievement that exist in a virtual space” (p. 2). They provide direct evidence of quality of training and the achievement of specific KSaOs for given RPA operations. The issuing of a digital badge can be performed by aviation regulators or by training organizations who provide the training.
Badging provides benefits for potential employers of RPAS operators, as the integration of assessment procedures outside those normally used within the aviation sector can be achieved through badging. For the RPAS sector and the need to supply proof of some form of flying competence, instead of documentation or even an onsite inspection, badging would allow for the submission of videos displaying the required RPAS KSaOs. In this scenario, not only are the syllabus items and criteria of the badge available for potential employers to view but the performance of the badge holder is also available for the employer to view [30].
As well as benefiting training providers and future employers, there are also benefits for students in having a system of digital badges for RPAS KSaOs. Newby and Cheng [30] note the ability of badging to provide scaffolding for the learner through “recognizing informal or granular learning experiences and serving as a roadmap for how specific experiences and learning could be sequenced for optimal learning” (p. 1056). These qualities are very beneficial for a learning area such as RPAS which is still fluid and needs further understanding for the KSaOs of competent and expert operators. Badging allows students to appraise the syllabus and the needed criteria for success, as well as the ability to submit supporting evidence that the criteria have been met [30].
Gamrat and Bixler [32] identify difficulties that must be overcome if digital badging is to deliver on its potential. Within an organization, much time must be put into understanding and communicating what the badges represent. This requires the issuing organization to be aware of variations in badge design, from a badge being issued for relatively insignificant achievements through to a badge for graduating with a PhD thesis. The assessment practices need to be well communicated to future participants as well as potential employers of those who achieve badges. Not only are these assessments known but they must also be valid and measure and assess those criteria they state they measure. Designing badges is complex, and more so by the need to meet the expectations of the different participants in the badging system, from the issuing authority to the students and on to the end user of future employers. “At first implementing digital badges can seem easy, but addressing these complexities can be overwhelming” [32].
Historically, aviation has based its competency-based assessment on the dual qualifications of a base license (e.g., a Commercial Pilot License) and a type rating for an aircraft type or class or for more specific competencies such as an instrument rating or an instructor rating.
This could be a way forward for the RPAS sector as the array of activities that RPAS can be used for will demand a greater range of credentials to recognize the different KSaOs for different types of operations. Digital badging could well fit easily into a suite of RPA credentials. A base operator’s certificate or license is (e.g., a RePL in Australia) issued by the regulator, and then badges can then be gained in the myriad of different RPAS operations, some of which are yet to be recognized or understood. The ICAO RPAS Manual (2015) recognizes the variations in different RPAS activities and the need to recognize these variations and differences.
To illustrate the usefulness of digital badges, an observational study of one type of RPA operation—agriculture drone flying—was conducted to identify those KSaOs that are required for this type of flying but not needed in other operations.
5. Methodology
The review of the literature of RPA competencies illustrates the continual use of experienced drone pilots to learn more about the underlying KSaOs of successfully and safely flying a drone. There is a range of methodologies that can be utilized to collect qualitive data. One of the suggested methods for capturing the KSaOs of RPA pilots is observational studies [22]. This can be performed with direct observation in the field, as Wiggins and Stevens [33] describe the use of observing a video of the studied performance as also being possible for the analysis. It is a means of obtaining data from natural environments, away from experimental set-ups in laboratories, providing a more realistic view of operational behavior. The disadvantage is the participants performing in a manner they normally would not, as they are not keen to show behavior that may be less than flattering, known as the audience effect [33]. Observing performance from pre-recorded video can reduce the audience influence. Wiggins and Stevens [33] also recognize the variabilities in field observations and lack of control as being constraints in using this type of methodology.
An analysis was conducted on drone pilots flying agriculture spraying operations to identify the relevant KSaOs of this category of operation. Eight publicly available videos of agriculture RPA operations utilizing multi-rotor aircraft were viewed. From this list, three videos were chosen for an in-depth observation of the actions of agriculture drone pilots. The three observed flights were chosen, as they were each conducted by a single pilot. A text narrative was created from these observations of the flights to identify the KSaOs displayed by the drone pilot. Qualitive data analysis can involve different media, especially with the ever-increasing prevalence of visual media; however, text remains the dominant type of data that is analyzed [34]. The text was turned into a word cloud to provide a viewable portrayal from largest to smallest of the main actions exhibited during the agriculture RPA operation, allowing them to be readily identified and analyzed.
6. Results
After observing the performance on three publicly available videos, the recorded text was turned into a Word Cloud for easy insight into the dominant actions of the pilots (Figure 1).
The identified KSaOs from the agriculture spraying flights are listed in Table 1. The mission of the operation was to spread the spray, containing a material such as herbicide or pesticide or even seeds, on a field of crops. The largest words in the Word Cloud confirm the importance of the mission with the words “drone”, “flight” and “spray” indicating they were recorded most often.
The next largest words indicate how the mission was achieved and can be listed into three sets of knowledge: aircraft equipment knowledge, operational knowledge and function or task knowledge.
The aircraft equipment knowledge refers to the knowledge and correct operation of the machinery being operated. With this knowledge, the RPA pilots were able to pre-flight the drone to ensure it was airworthy and could complete the task required. The pilots were able to calculate the endurance of the batteries to plan the routing that the aircraft would follow to ensure accurate application of the spray to the desired areas. Changing the batteries in a timely manner was part of this planning. The Ground Control Station (GCS) was used to perform both planning tasks and flying tasks and while the flying observed in the videos was autonomous, there was a realization by the pilots that they may, at any time, need to take manual control of the drone using the GCS.
Operational knowledge refers to being able to perform the operation in a skilled, safe and legal manner and includes planning the performance parameters of the flight. Prior to the flight, the boundary of the field to be sprayed was marked out on the screen of the GCS. From within the boundary, the flight route could be laid down. The marking of the boundary was performed using existing satellite views of the locality, or one of the operators flew the drone around the boundary which was recorded on the GCS. This included the length of time the drone could stay in the air for, before returning home, and the distance that could be covered in that time for the speed that the drone was flying at. The height of the drone played an important part in this planning, as the lower the drone was to the ground, the better the spray coverage was but the longer it would take to complete coverage of the field, while the higher the drone was, the quicker the field would be sprayed, but the negative was a reduction in the spray coverage. Once the performance parameters were set and the height of the drone and route to be flown were set, the aircraft could be started and then it flew autonomously with no direct intervention from the pilot while remaining in the sight of the pilot. The flight pilots continually monitored the drone until it returned autonomously to the home point.
These two sets of knowledge and skills are standard to most, if not all, RPA operations and will form the basis of a syllabus for licensing purposes.
The function/task knowledge is the reason for conducting the operation and the specific KSaOs required for this agriculture flying task of spraying the allotted field. They will not likely be competencies required for other operations. Prior to launch, the swarth needed to be calculated. This refers to the disbursement of the spray and the area it will cover. The variables affecting the swarth included the droplet size, the height of the drone, and its ground speed. Finally, there was the filling of the spray tank after the completion of each flight with an attendant safety concern.
7. Discussion
The number of roles that can be filled by RPAS is large and ever expanding as identified by Herdel et al. [6]. While there remain differences in national regulators acknowledging RPA piloting competencies [11], there has been growing development in the understanding of RPAS competencies [22,23]. These KSaOs are often of a generic type that applies to almost all drone operations and forms the basis of training syllabi and licensing requirements. From the snapshot provided by this study, there are functional differences between tasks and KSaOs that are additional to the generic KSaOs and are specific to each type of operation.
Ljungblad et al. [23] identify the importance of the task the drone is conducting, as the drone has become a tool of the profession. The KSaOs for this professional task can be unique to the task and useful only for the task. Therefore, a more nuanced approach is needed in developing an understanding of what RPA KSaOs may be required for each flying job. The ability to provide confidence to the marketplace that a licensed RPAS operator is competent in both these generic and specific KSaOs for the operation they will be performing is challenging. Digital badges have the facility to be able to achieve this.
To illustrate one example of the usefulness of digital badging for RPAS operations, an observational study was conducted to identify KSaOs, using videos of drone flights, where the RPA was used to agriculturally spray a field with product. The observed KSaOs from this type of operation could be grouped into two categories, generic and specific. The generic KSaOs are those that would be utilized for any drone operation and include knowledge of the aircraft and its systems and the operational knowledge of planning the performance of the operation with such variables as time, speed and endurance. The exercising of these competencies was performed according to Ljungblad et al.’s [23] typical drone mission of planning, flying, and data output. The subjects of the observed videos carried out the KSaOs in three sections, away from the flying area, prior to the flying operation in consultation with the client (in this instance, the farmer), and during the flight in the field, and then in the follow-up of providing a report to the farmer.
In this example of agriculture spray operations, a competency specific to this operation but not needed in other operations was identified in the planning of the spray swarth, that is, the knowledge of variables such as droplet size, weather conditions, and height of the drone affecting the application coverage. While there are agencies outside aviation that have requirements for the handling of sprays and the aircraft that disperse the spray, there is still an aviation competency required in the knowledge and practice of spraying a farmer’s paddock.
8. Conclusions
With the ever-growing number of tasks RPA can be used for and the increasing variety of drones to perform these tasks, there is a requirement to identify and understand the knowledge, skills, and other attributes needed to be exercised by RPA operators to ensure safe and efficient flight. Although there were early starts to this process, they have not always been known and used for the training of potential RPA operators. This combined with ICAO providing only guidance material for RPAS (as against SARPS for conventionally crewed aircraft) has resulted in developed differences between national regulators despite ICAO having an expectation that national bodies will align their drone licensing with the established licensing system set out in Annex 1 [11]. Those differences can be illustrated by very prescriptive syllabi for license acquisition (e.g., Australia) as compared to more relaxed requirements (e.g., New Zealand).
The interest in identifying RPAS KSaOs is building, with Torrance et al. [17] finding 88 articles addressing the topic and helping to provide knowledge of what is required to operate a drone safely. These KSaOs tend to be all-encompassing, covering the requirements for the broad range of potential RPA usage. However, flying a drone is often performed for specific purposes, for the professional tasks needing to be undertaken by the operator, and there is a need to further understand and develop distinct KSaOs for the specific task being undertaken. The base license may not cover these specific competencies; thus, there is a need to provide evidence of mastery of the specific competencies. Ljungblad et al. [23] and Dalilian and Nembhard [28] find that flying the unmanned aircraft is only part of what is required in RPA flying skill acquisition, as the task for which the operation is conducted can be more demanding than the piloting skills.
It is suggested that digital badges may be one option to provide this evidence. The badges can be linked to micro-credentials, providing specificity while being easy to use for RPA operators and employers. As blast engineers, agriculture sprayers, building inspectors surveyors, and many other type of RPA operators look to exercise their knowledge and skills, a digital badge indicating mastery of the specific KSaOs provides assurance that they are competent to complete the tasks.
Conceptualization, J.M. and G.W.; methodology, J.M. and G.W.; software, G.W.; validation, J.M., G.W. and K.J.; formal analysis, J.M. and G.W.; investigation, J.M.; resources, G.W.; writing—original draft preparation, J.M.; writing—review and editing, J.M., G.W. and K.J.; visualization, J.M. and G.W.; supervision, G.W. and K.J.; project administration, G.W. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data will be made available on request.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
KSaOs of agriculture spraying flights.
| Agriculture Spraying Operation | ||
|---|---|---|
| Aircraft Equipment | Operational | Function |
| 1. Pre-flight check | 1. Flying performance | 1. Boundary marking |
| 2. Battery endurance/time and charging | 2. Time aloft | 2. Swarth calculation |
| 3. Ground Control Station usage | 3. Distance covered | (2a) Droplet size |
| 4. Route and height | (2b) Drone speed and height | |
| 5. Autonomous flight | 3. Handling chemical spray and filling spray tank | |
| 6. Monitoring | ||
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; Wild, Graham 3 1 School of Engineering and Technology, UNSW, Canberra 2612, Australia
2 Capability Systems Centre, UNSW, Canberra 2612, Australia;
3 School of Science, UNSW, Canberra 2612, Australia;