Yazan Mualla
email: yazan.mualla@utbm.fr

Amro Najjar

Alaa Daoud

Stéphane Galland

Christophe Nicolle

Ansar-Ul-Haque Yasar

Elhadi Shakshuki

Agent-based simulation of unmanned aerial vehicles in civilian applications: A systematic literature review and research directions

Keywords: Multi-Agent Systems, Agent-Based Simulation, Unmanned Aerial Vehicle, Systematic Literature Review, Civilian Applications

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Unmanned Aerial Vehicles (UAVs), most commonly known as drones, are 2 becoming increasingly popular for civilian applications in several domains such 3 as agriculture, transportation, products delivery, energy, emergency response, 4 telecommunication, environment preservation and infrastructure. According to 5 Teal Group's 2018 World civilian UAV Market Profile and Forecast report [START_REF] Group | World Unmanned Aerial Vehicle Systems -2018 Market Profile and Forecast[END_REF], civilian UAV production will total US$88.3 billion in the next decade, with a 12.9% compound annual growth rate. The same report states that the civilian UAV sector promises to be the most dynamic growing sector of the world aerospace industry in the following years. Furthermore, fueled by a growing demand from governments and private consumers, civilian UAV market is expected to quadruplicate over the next decade. The key features making the UAVs interesting to use are their small dimensions, ability to take-off and land vertically, good maneuverability, simple mechanics, and payload capability. These features make UAVs accessible for civilian applications deployed in urban environment where they started to be used as a practical solution for cost-efficient and rapid delivery. One of the mostly known examples is Amazon Prime Air where UAVs are used to deliver packages to customers [START_REF] Bamburry | Drones: Designed for product delivery[END_REF][START_REF]Guest editorial can drones deliver?[END_REF]. Another important example that comes from the health-care sector is the transportation of medical samples and products using a UAV health-care delivery network [START_REF] Scott | Models for Drone Delivery of Medications and Other Healthcare Items[END_REF]. Even though there were some concerns about the safety of this procedure in such transportation environments, it has been shown that the UAV transportation systems are a viable option for the transportation of medical samples and products [START_REF] Amukele | Drone transportation of blood products[END_REF].

Another notable example comes from the Oil&Gas industry. The tragic Deepwater Horizon accident in the Gulf of Mexico in 2010, as well as the increase in deepwater offshore activity, have increased public interest in Health, Safety and Environment (HSE) counter-measures for sub-surface releases of hydrocarbons. To attain a proper contingency planning, response managers urge for a system allowing instant detection and characterization of accidental releases. To meet these requirements, a heterogeneous robotic system involving UAVs was developed for early detection of hydrocarbons [START_REF] Vasilijevic | Heterogeneous robotic system for underwater oil spill survey[END_REF].

Despite these initial successes, UAV technology is still in its early stages of development. For this reason, considerable limitations should be addressed before a large scale deployment of UAVs in civilian applications is possible. The main drawbacks to mention are related to the safety of humans facing UAV failures [START_REF] Wild | Exploring Civil Drone Accidents and Incidents to Help Prevent Potential Air Disasters[END_REF], and the high amount of energy consumed by these devices coupled with their limited battery life [START_REF] Rodić | Modeling and simulation of quad-rotor dynamics and spatial navigation[END_REF]. Moreover, since civilian applications are mostly deployed in urban environments involving multiple actors, considerable research efforts should be dedicated to enhancing the UAV perceptual intelligence required to coordinate complex environments [START_REF] Floreano | Science, technology and the future of small autonomous drones[END_REF], and more importantly to address the possible consequences, especially on people safety, of a mechanical failure that may cause a crash and the costs of such incident.

To guarantee it is safe for UAVs to fly over people's heads and to reduce costs, different scenarios must be modeled and tested. However, currently, most of the regulations in force restrict the use of UAVs in cities. For this reason, to perform tests with real UAVs, one needs access to expensive hardware and field tests that are costly, time-consuming, and require trained and skilled people to pilot and maintain the UAVs. Moreover, in the field, it may also be hard to reproduce the same scenario several times [START_REF] Lorig | Measuring and Comparing Scalability of Agent-Based Simulation Frameworks[END_REF]. To overcome these limitations, simulation frameworks have been developed to allow transferring real-world scenarios into executable models (i.e. simulating UAV activities in a digital environment).

An agent is defined as an autonomous software entity that is situated in some environment and where it is capable of actions and coordination with other agents to achieve specific goals [START_REF] Wooldridge | An introduction to multiagent systems[END_REF]. For these reasons, the resulting Multi-Agent System (MAS) technology have been established as a suitable platform for implementing autonomous behavior and decision-making in computer systems [START_REF] Weiss | Multiagent Systems (Intelligent Robotics and Autonomous Agents series)[END_REF][START_REF] Sycara | Multiagent systems[END_REF]. An Agent-Based Simulation (ABS) model is a set of interacting intelligent entities that reflect, within an artificial environment, the relationships in the real world [START_REF] Wooldridge | Intelligent agents: Theory and practice[END_REF]. The results make ABS a natural step forward into understanding and managing the complexity of today's business and social systems.

Despite this promising research efforts, very few works were dedicated to understand and analyze existing works using ABS in civilian UAV applications.

Very few surveys outlined a comprehensive set of research questions pertaining to multi-agent simulations for civilian UAV applications.

Against this background, the objective of this paper is two-fold:

1. Conduct a Systematic Literature Review (SLR) of the research addressing MAS in civilian UAV applications. This SLR aims to identify the most important research questions, analyze the literature, and outline key challenges and research directions.

Outline and discuss the research directions of UAVs in civilian applications

To the best of our knowledge, no systematic literature study has been conducted to review the research addressing ABS in civilian UAV applications.

The rest of this paper is organized as follows: Section 2 states and defines the SLR methodology adapted from [START_REF] Budgen | Performing systematic literature reviews in software engineering[END_REF][START_REF] Kitchenham | Refining the systematic literature review process-two participant-observer case studies[END_REF]. Section 3 details the analysis and results of the SLR. Based on the results of the SLR, Section 4 identifies the major research directions. Section 5 discusses the related works. Section 6 concludes this paper and identifies future research perspectives.

SLR Methodology

Recently, research on computer science in general and on artificial intelligence, in particular, has witnessed a significant increase both qualitatively and quantitatively. For this reason, SLRs are becoming popular to help analyze the evolutions of these domains. Kitchenham and Charters [START_REF] Kitchenham | Guidelines for performing Systematic Literature Reviews in Software Engineering[END_REF] define SLR as follows: "A form of secondary study that uses a well-defined methodology to identify, analyze and interpret all available evidence related to a specific research question in a way that is unbiased and (to a degree) repeatable." Where secondary study refers to "a study that reviews all the primary studies relating to a specific research question." In this paper, we define a primary study as a research paper addressing a specific research question in the domain of UAVs.

The aim of SLRs can be threefold [START_REF] Kitchenham | Guidelines for performing Systematic Literature Reviews in Software Engineering[END_REF]: (i) to summarize the existing evidence concerning a specific technology that is being used broadly, (ii) to identify gaps in the existing research in order to suggest areas for future investigation, and (iii) to provide a background allowing to position new research activities. [START_REF] Brereton | Lessons from applying the systematic literature review process within the software engineering domain[END_REF][START_REF] Galster | Variability in software systems-a systematic literature review[END_REF]).

With these goals in mind, we base our systematic literature review on [START_REF] Budgen | Performing systematic literature reviews in software engineering[END_REF][START_REF] Kitchenham | Refining the systematic literature review process-two participant-observer case studies[END_REF], which are among the most common methodologies for computer science SLRs. Such an approach ensures rigorousness, fairness, and reproducibility.

Figure 1 illustrates the review process. This section is organized as follows. First, Section 2.1 highlights the research questions. Second, Section 2.2 explains the review protocol, how conflicts are resolved and biases overcome. Third, in Section 2.3, the defined protocol is executed and the review process is undertaken (document collection, conflict resolution, etc.).

Key Research Questions

Following the Goal Question Metric (GQM) [START_REF] Kitchenham | Refining the systematic literature review process-two participant-observer case studies[END_REF], we define our generic freeform question as "Discover and evaluate the possible scientific MAS contributions to the civilian UAV applications." On the field, it may be hard to deploy UAVs because of safety and security issues. Moreover, it may be difficult to reproduce the same scenario several times in order to test hypotheses and validate the UAV behaviors. ABS is a suitable tool for overcoming these limitations. In order to reflect this aspect, the generic free-form question becomes "Discover and evaluate the possible scientific ABS contributions to the civilian UAV applications." This question is broken down into Research Questions (RQs) exploring key issues in ABS for civilian UAV applications. The research questions are mainly concerned with this type of applications. More specifically, these questions cover the purposes, issues, used simulation frameworks, publications date, authors, countries, etc. These questions were formulated based on the authors' knowledge in the UAV and ABS domains as well as the common practices from other SLRs. In what follows, eight RQs are considered within this review, and listed below: RQ1: Understand the evolution of UAV simulations in MAS in the last decade in terms of key contributors (research labs), geographic distributions, growth over the years. Having answers to this RQ will help researchers determine the liveliness of the MAS modeling domain for civilian UAV applications. Moreover, this RQ will enable highlighting the active contributors. In this way, it may help researchers to find quickly new contributions in the domain.

RQ2: Identify the main UAV research topics and civilian application domains addressed in the studied papers. On one hand, we consider it is important to determine active research topics in order to highlight less active research topics where more contributions are needed. On the other hand, and as the market of civilian UAVs is expanding rapidly in several application domains [START_REF] Group | World Unmanned Aerial Vehicle Systems -2018 Market Profile and Forecast[END_REF], a synthetic view of these application domains will enable researchers to determine the typical applications for their research, and possibly identify new application domains.

RQ3: Identify the artificial intelligence models scaffolding the solutions in the reviewed papers. This RQ is set up in order to have a view on the types of agent architectures used for implementing civilian UAV applications.

It also helps researchers understand the potential of these agent & system architectures, and their limitations. The following sub-questions focus on specific types of models and architectures.

Defining The Review Protocol

As shown in Fig. 1, defining the review protocol is done right after having set the RQs. The protocol we used for this article involves the following steps. First Section 2.2.1 chooses the databases used as sources of information and defines the stop criterion. Section 2.2.2 defines the exclusion/inclusion criteria used by the reviewers to exclude/include articles chosen from the databases before the stop criterion was triggered. Section 2.2.3 presents the quality criteria used by the reviewers to assess the quality of the primary studies. Finally, Section 2.2.4 explains the policies used to mitigate subjective biases and resolve conflicts.

Database Selection

This process is composed of the following couple of steps: a) IEEE Xplore, ACM Digital Library, and Google Scholar are selected as the three databases constituting the source of information. The selection of the first two databases is obvious in computer science. Google Scholar is selected because it provides a large list of documents that are not indexed into the two previous databases, e.g. papers from conference proceedings, Ph.D. and Master theses. Despite not being peer-reviewed, these articles obtained from Google scholar might be important given that the interest in the studied topic is rapidly increasing in the recent years.

b) The databases are queried with a set of keywords. These keywords are devised based on the authors knowledge of the UAV and MAS domains.

When queried with these keywords, each database responded with a set of articles that are considered by the reviewing process. The numbers of articles to be produced by the queries is relatively large for IEEE Xplore, ACM DL, and Google Scholar databases. However, only a few of these articles were relevant to the research questions raised in the previous section. For this reason, as in [START_REF] Calvaresi | Exploring the ambient assisted living domain: a systematic review[END_REF], the following stop criterion was applied: "Stop the collecting of articles after a sequence of 10 titles, completely incoherent with the query, appeared in the list." Determining whether an article is coherent is left to the reviewers' subjective view when they deemed that there was no adherence between the query performed on the database and the title/abstract of the article appearing in the result.

Inclusion and exclusion criteria definition

The articles appearing in the resulting pool of articles are not necessarily useful to answer the research questions defined above. For this reason, most of literature review methodologies [START_REF] Galster | Variability in software systems-a systematic literature review[END_REF][START_REF] Calvaresi | Exploring the ambient assisted living domain: a systematic review[END_REF] ExC2: Invalid type of paper, the document is a poster or a demo -It is assumed that a poster or a demo cannot give enough details on the contributions, as there is no enough contributed content for evaluation.

Ph.D. theses, Master theses, technical reports are included.

ExC3: Invalid type of paper, the paper is a survey -It is assumed that the survey papers (i.e. secondary studies) do not provide contributions directly on the UAV models nor UAV technologies.

ExC4: Impossible to access the paper text -It is impossible to evaluate a paper when its text cannot be accessed (PDF download, online text, etc.)

ExC5: Extended paper -The paper is extended by another paper by the same authors. The contributions in the extended paper are enclosing the ones from the original paper, so that the latter is excluded.

ExC6: Unrelated to UAV -The paper has no contribution neither in the fields of UAV models nor UAV technologies.

ExC7: Unrelated to agent-based systems -The paper has no contribution in the fields of agent-based technologies nor distributed artificial intelligence.

Generally, only multi-agent applications are included, but if the system includes agents that communicate with other entities like Infrastructure (refer to ExC14), then they will also be included.

ExC8: UAV manufacturing only -The paper's contribution is related to the manufacturing of UAVs, i.e. it is related to the design and implementation of hardware, mechanic or electronic components. ExC9: Positioning system only -The paper's contribution is related to the definition of novel positioning systems within UAVs. The contribution focuses on a perception model that enables each UAV to compute its position in the air.

ExC10: UAV detection system only -The paper's main concern is UAV detection within the system. In other words, the contribution is not related to UAV behavior, but to a system that is detecting the UAV in the air.

ExC11: No civilian application -The paper contains only military applications that cannot be applied to civilian fields.

ExC12: No simulation contribution -The paper's contribution cannot be applied to UAV simulation. In several papers, the model is deployed on real UAVs without simulation. Even if the paper has not a direct contribution to UAV simulation, if the proposed model could be deployed within a simulation environment, the corresponding paper is not excluded.

ExC13: Simulation in 2D -The paper's contributions include a simulation model in 2D that cannot be extended to the third dimension. It is assumed that 2D simulation of UAVs cannot achieve the highly detailed reproduction of the UAV behavior when they are in the air. Additionally, they do not provide detailed environment model. However, to estimate the portion of 2D and 3D simulations, we keep track of this exclusion criterion, and plot how many papers were excluded due to being based on 2D simulation (c.f. Section 2.3).

ExC14: No UAV cooperation nor interaction -The paper contains a contribution related to neither the cooperation of UAVs nor interaction between UAVs. V2I and V2V communications are assumed to be the base framework for supporting UAV interaction. If a paper contains a model for a single UAV that has V2I communication, it is not excluded since this type of models could be duplicated in order to set up a more complex simulation environment based on stigmergy1 communication.

ExC15: Human interaction with no autonomous UAV -The paper contains only a contribution related to the interaction between humans and the Is there an adequate description of the context in which the study has been conducted? Q3

Is there a clear statement of the findings and the results including data that support the findings? Q4

Are the limitation of the study discussed and highlighted?

UAVs, for piloting or controlling the UAVs. In other words, the contribution focuses on the human-UAV interaction without autonomous control of the UAVs.

These exclusion criteria are applied to the documents in two steps. In the first coarse-grained step, the articles were only eliminated if their titles and abstracts satisfied at least one of the exclusion criteria. In the second finegrained step, the remaining papers are screened but this time reading the whole body of the paper.

Quality Criteria

As has been recommended by Kitchenham and Charters [START_REF] Kitchenham | Guidelines for performing Systematic Literature Reviews in Software Engineering[END_REF] and Kitchenham et al. [START_REF] Kitchenham | An Evaluation of Quality Checklist Proposals-A participant-observer case study[END_REF], most of the SLRs rely on quality criteria allowing to assess the quality of primary studies, (c.f. [START_REF] Galster | Variability in software systems-a systematic literature review[END_REF][START_REF] Calvaresi | Exploring the ambient assisted living domain: a systematic review[END_REF] for another example). Defining quality criteria as a list of questions is a common practice. Typical quality criteria include: (i) whether the authors of primary studies provided a sound rationale for their work, (ii) details provided about the context and the design of the technical evaluation, (iii) the statement of the results.

Note that, as it is the case in [START_REF] Galster | Variability in software systems-a systematic literature review[END_REF], the quality criteria are not used to exclude/include primary studies. Rather, they are used to report the overall quality of primary studies included by the SLR. To assess the quality of the reviewed works, Table 1 defines four quality questions, adapted from [START_REF] Galster | Variability in software systems-a systematic literature review[END_REF]. Note that Q3 is of particular interest since having an overview of the quality of evaluations of the set of articles dealing with a specific research question can give a good idea on the maturity of this research question.

Biases and disagreements

In order to mitigate the subjectivity of the reviewing process, certain measures were taken to overcome biases and resolve conflicts. In particular, each task of Phase 2 in Fig. 1 was conducted by at least 2 reviewers. Thus, as shall be discussed later, the steps of article exclusion/inclusion (c.f. Section 2.2.2), answering the RQs, and quality assessment (c.f. Section 2.2.3), were undertaken by at least two reviewers for each article. A third reviewer intervened as a referee to resolve a conflict in the exclusion/inclusion and in the RQ answering steps. As for the quality assessment task, quality assessments provided by reviewers for each article were averaged.

Performing The Review

This section gives an account of how the SLR has been conducted and discusses the results of the exclusion/inclusion step. Two keyword sets were applied to the three databases (IEEE Xplore, ACM DL, and Google Scholar). The first keyword set is {UAV, agent, simulation}, whereas the second keyword set is {UAV, agent-based simulation, drones, civil, multi-agent systems}. A stop criterion of 10 articles was applied to the results of each keyword set and database. After the stop criterion was applied, the total number of articles retained was 316. The next step is to apply the coarse-grained exclusion/inclusion step. Note that since this step screens papers based on their titles and abstracts, some exclusion criteria might be more helpful than others (e.g.ExC1 and ExC2).

Table 2 shows the results of the coarse-grained exclusion/inclusion step. As can be seen from the third row in the table, the total number of papers acquired from the two sets combined is 316 papers. The results listed in Table 2 show that about 39% (123 papers) of the total number of papers were included by the coarse-grained exclusion/inclusion step. This fact may explain the high number of publications for this country. From Fig. 2, it is interesting to note that, even if civilian UAV-related regulations in USA are less restrictive than in Europe (c.f. Section RD6), the number of papers over Europe is two times higher than the number of papers in North America. This can be attributed to the EU research policy that enforces funding on breaking technologies, such as UAVs. Fig. 4 plots the number of papers per year since 2008 after the coarse-grained exclusion/inclusion step. The number of papers grows with a slop of 0.6364.

The next step within the review process is the fine-grained exclusion/inclusion step. It is applied to the 123 papers selected during the previous step. More specifically, the content of the paper is screened and the paper is excluded if it satisfies at least one of the 15 exclusion criteria defined in Section 2.1. Table 3 shows the results of the fine-grained exclusion/inclusion step. Each paper was reviewed by at least two reviewers. If all the reviewers of a paper decided that it should be included in the review, the paper is included (c.f. the first row of the table). The paper is excluded if all of its reviewers agreed upon its exclusion (the second row of the table). Otherwise, in case of a conflict among reviewers, we relied on a referee to resolve this conflict. If the referee accepts the paper, then it is included in the review. As shown in the table, 23 papers (about 19% of the total number of the papers) generated conflicts among the reviewers. Out of these papers, 12 papers were added by the referee raising the total number of included papers to 42 (about 34% of the papers remaining after the coarse-grained exclusion/inclusion step). For a list of the 42 papers, please refer to Table 6.

Most of the papers screened in the fine-grained step were highly related to the RQs. As indicated by Table 3, 66% of them were excluded, and Fig. 5 plots the percentage of excluded papers per exclusion criterion. Note that some papers were excluded because of satisfying multiple exclusion criteria.

Examining the reviews shows that approximately 24% of excluded papers were excluded because of ExC13, which filters out a paper if it is not addressing 3D simulation scenario. simulations is witnessing a confirmed and significant increase (2018 should not be considered since this review was conducted in August 2018). This is mainly due to the fact that UAV systems are intrinsically in three dimensions, and this third dimension must be included within the models in order to evaluate the behaviors of the UAVs regarding the safety concerns. Additionally, simulating

UAVs in a 2D model forbids the deployment of the algorithms in real UAVs except in very specific cases in which the altitude of the UAVs is constant.

Nevertheless, in this later case, the deployed algorithms must ensure that the UAVs remain at the same altitude regardless of the environment conditions.

This section offered a detailed account of how the review was performed and provided useful statistics about the included/excluded papers. Furthermore, it discussed the most common exclusion criteria. Next section presents and discusses the results of the SLR regarding the research questions presented in Section 2.1.

Results of the Review and Analysis

This section provides a detailed analysis of the SLR results. It analyzes the papers retained after the fine-grained exclusion/inclusion step and discusses each one of the RQs defined in Section 2.1 respectively. Note that the results are related to MAS in civilian UAV applications and are derived from the research questions and the exclusion criteria defined in the SLR methodology.

Demographic Data (RQ1)

To understand the evolution of UAV simulations in MAS in the last decade funding projects. This fact may explain the high number of publications for this country compared to its R&D investment, illustrated in Fig. 10. In the mentioned figure, it is interesting to note that, even if civilian UAV-related regulations in USA are less restrictive than in Europe, the number of papers over Europe (32 papers) is three times higher than the number of papers in North America (9 papers). We explain this by the fact that the EU research policy enforces funding on breaking technologies, such as UAVs. Additionally, with a relatively lower R&D investment rate of EU countries (Fig. 10), researchers from these countries have fewer opportunities for funding UAV deployment on real fields. This pushes them to resort to simulation environments for validating the UAV behaviors before any deployment.

Research Topics & Application Domains (RQ2)

This section discusses the results and answers the research questions raised in RQ2 (c.f. Section 2.1). In particular, it deals with the research topics (Section 3.2.1) and the application domains (Section 3.2.2).

Research Topics

Subjects or issues that a researcher is interested in when conducting research on UAVs. These "research topics" provide the general directions to researchers for exploring, defining, and refining their ideas. Fig. 11 plots the main research topics addressed in the reviewed articles, and Therefore, as Fig. 11 shows, the research topics have diverse aims and tackle several aspects related to UAVs, ranging from low-level (i.e. close to hardware) issues, such as UAV communication, to high-level concerns that require considerable UAV autonomy (e.g. mission management).

In order to assess the UAV autonomy involved in each research topic, we rely on an autonomy metric proposed by Clough [START_REF] Clough | Metrics, schmetrics! How the heck do you determine a UAV's autonomy anyway[END_REF] to measure Autonomous Control Levels (ACL) of UAVs. This metric is a scale ranging from 0 (for a remotely piloted non-autonomous UAVs) to 10 (for a fully autonomous UAVs).

Fig. 12 plots the average ACL for each research topics. Note that ACL values were either mentioned explicitly by the authors of the primary studies or were determined by the reviewers by evaluating the UAV autonomy according to the ACL scale.

As can be seen in Fig. 12, some research topics tended to endow UAVs with more autonomy than others. For instance, coverage, coordination, surveillance, and mission management need more autonomy than path planning, collision avoidance and communications.

Note that research topics such as certification & regulation and simulation attained relatively high ACL. However, the main concerns of these works were building a simulation environment for the UAVs (in case of the simulation research topic) and certifying that UAVs adhere to the enforced regulations & norms (in case of the certification & regulation research topic). For this reason, the UAVs implementation provided by these works were mainly case-studies lacking details about the evaluations and the implementations.

Furthermore, no reviewer reported a paper with an ACL scale higher than 6. This is explained by the fact that higher levels of autonomy in the ACL scale were associated with specific military application requirements (e.g. battlespace knowledge, battle-space cognizance, etc.), and this SLR focuses on work related to civilian applications only. Note that this issue could be solved by relying on other metrics allowing to evaluate the maturity of the contributions.

Technology Readiness Level (TRL) [START_REF] Mihaly | From NASA to EU: the evolution of the TRL scale in Public Sector Innovation[END_REF] is one metric that would help in this direction. Yet, unlike the ACL, it is not focused on UAV autonomy. For this reason, we opted for ACL in this paper.

Application Domains

In addition to the research topics, RQ2 addresses the application domains of UAVs. They refer to the applied research, in which scientific studies and research works aim to solve practical problems in specific application domains. Fig. 13 shows the distribution of the civilian UAV application domains of studied papers, and Table 5 lists the papers per each application domain. It is worth mentioning that some papers tackle several application domains. Therefore, and to normalize the weights given by each paper to the application domains distribution, we have decided to assign the most dominant application domain per paper. For the paper that has no dominant application domain, or if its contribution is application independent, it is considered to be in the General In order to assess the maturity of the reviewed primary studies, we resorted to the quality criterion Q3 defined in Table 1. Q3 evaluates the quality of the experiments conducted by the authors of primary studies and the statements of the obtained results. The intuition here is that the more mature the application domain is the higher would be its score for Q3. given by reviewers per application domain. The second cluster represents less mature application domains (general, urban planning, and border surveillance). Note that these assessments only concern works using ABS for civilian UAV applications.

Artificial Intelligence or Agent Architecture Type (RQ3)

This section deals with the RQ3. First, in Section 3.3.1 the used agent architectures are identified and discussed. Second, in Section 3.3.2, the used system architectures are analyzed. Finally, Section 3.3.3 discusses how the primary studies dealt with the dynamicity of the environment.

Agent Architecture (RQ3.1)

The results of the SLR concerning the agents' architectures showed that agents used in the studied research works, mainly fall into five categories:

(i) Reactive agents, (ii) Flocking agents, (iii) Belief-Desire-Intention (BDI) agents, (iv) Agents using cognitive architectures, and (v) Evolutionary agents.

The following list presents these architectures respectively.

• Reactive agents: The behavior of reactive agents is driven by their reactions to external stimuli (e.g. a message from another agent) or a change in their environment (a perceived obstacle).

Within the reactive agent behaviors, Flocking agents behavior is the behavior exhibited when a group of agents, e.g. birds, fishes are moving together. Basic architectures of flocking behavior are controlled by three simple rules: (i) separation for avoiding crowding neighbors, (ii) alignment for steering towards average heading of neighbors, (iii) cohesion for steering towards average position of neighbors. With these three simple rules, the flock moves in an extremely realistic way, creating complex motion and interaction that would be extremely hard to create otherwise.

• Cognitive agents: Agents of this type rely on cognitive architecture.

The latter aims at describing human cognitive processes as precisely as possible. In contrast to BDI, whose main inspiration is philosophical and relies on Michael Bratman's theory of human practical reasoning and on modal logic [START_REF] Bratman | Intention, Plans, and Practical Reason, Center for the Study of Language and Information[END_REF], cognitive architectures are inspired by an in-depth understanding of the human brain from biological and neurological perspectives.

There are many implementations of cognitive architectures. Soar [START_REF] Laird | The Soar cognitive architecture[END_REF] is one of the widely used ones.

A special type of cognitive agents are BDI agents. They are rational agents that having a "mental attitudes" of Beliefs, Desire, and Intention representing respectively the information, the motivational, and the deliberative states of the agent [START_REF] Rao | BDI agents: from theory to practice[END_REF]. BDI agents are capable of integrating planning, scheduling, execution, information gathering, and coordination with other agents [START_REF] Sycara | Multiagent systems[END_REF].

• Evolutionary agents: They are agents that are based on evolutionary algorithms. An evolutionary algorithm is a subset of evolutionary computation [START_REF] Vikhar | Evolutionary algorithms: A critical review and its future prospects[END_REF], a generic population-based meta-heuristic optimization algorithm. It uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.

Fig. 16 shows the agent architectures used in the papers. As can be seen from the figure, cognitive architectures were the most common among the primary studies (≈ 41%) followed by Reactive agents (≈ 22%) and BDI (≈ 14%).

This result shows that proactive agents, those agents capable of goal-directed behavior (BDI agents, and cognitive agents) constitute about 55% of all the analyzed studies whereas reactive agents (including those with flocking behavior) are less common (≈ 24% of the analyzed works). This shows that most of the research works seek to equip the UAVs with greater autonomy and goal-directed behavior.

In order to understand the correlation between the agent architectures and the autonomy, Fig. 15 shows the average ACL per agent architecture (c.f. [START_REF] Clough | Metrics, schmetrics! How the heck do you determine a UAV's autonomy anyway[END_REF] and Section 3.2). The BDI agents offered the highest level of autonomy, followed by cognitive agents, evolutionary agent, and lastly reactive agents. This result confirms our expectations and suggests that cognitive architecture and BDI agents are promising paradigms allowing to build more autonomous UAVs. The other papers focus on a single layer, mostly tactic or strategic.

Decentralization/Centralization (RQ3.2)

Fig. 18 shows the number of papers with reactive / direct collaboration system model, and with the system architecture used by the model (decentralized / centralized). As expected, the majority of the papers are related to decentralized architectures, which correspond to one of the major characteristics of MAS and UAV systems. However, 12 papers contain proposals that correspond to a centralized architecture, i.e. the model contains a central agent, or the model is formalized in such a way that it could be implemented only with centralized frameworks, e.g. Simulink [START_REF] Ozpineci | Simulink implementation of induction machine model-a modular approach[END_REF] (c.f. Section RQ8).

Environment Dynamicity (RQ3.3)

Immersing agents in dynamic physical, virtual or mixed environments is still a challenge for MAS researchers. As has been established in [START_REF] Weyns | Environment as a First-class Abstraction in Multi-Agent Systems[END_REF], an essential part of such systems is the MAS environment, in order to provide the services allowing agents to interact with it. However, defining what is the interface between the agents and their environment is not obvious. A key aspect is to both respect their autonomy and ensure that the environment rules are enforced.

Weyns et al. [START_REF] Weyns | Environment as a First-class Abstraction in Multi-Agent Systems[END_REF] define agent environment as the software layer between the external world and the agents.

Dynamic agent environments include endogenous processes that enable the environment's state to evolve dynamically outside the control of the agent. In a static agent environment, such process is not included. Additionally, the agent environment state could evolve only as a consequence of the agents' actions. If an action is never applied to the agent environment by the agents, it is passive. Agent environments in most of the reviewed papers are passive. In these cases, UAV missions are mainly surveillance, collision detection, coordination, etc. However, as UAVs become involved in more application domains where acting on the environment is necessary, there are likely to become more active to the agent environment.

UAV Environment with IoT (RQ4)

The smart city concept integrates Information and Communication Technology (ICT) (c.f. Section RQ5), and various physical devices connected to the network, e.g. IoT or Wireless Sensor Network (WSN) [START_REF] Glaser | Smart Cities Growth Opportunities Overview 2016-2021[END_REF].

Fig. 21 shows the proportion of papers that include the IoT concept against the papers that do not. 9 papers include IoT, and 33 do not. According to [START_REF] Glaser | Smart Cities Growth Opportunities Overview 2016-2021[END_REF], several opportunities for UAVs use to support a smart city exist. These opportunities will be very beneficial to any smart city that would utilize UAVs for their economic growth and development. Therefore, it is important to investigate whether IoT is considered in the reviewed models. The low proportion of reviewed papers that are considered IoT indicates that it is still an open issue.

According to our knowledge, this proportion may be explained by the fact that researchers focus on the UAV behavior itself, not on the UAV environment.

UAV Interaction and Communication (RQ5)

Vehicle-to-everything (V2X) communication is the passing of information from a vehicle to any entity that may affect the vehicle and vice versa [START_REF] Weií | V2X communication in Europe -from research projects towards standardization and field testing of vehicle communication technology[END_REF]. It is a vehicular communication system that incorporates other more specific types of communication as Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Device (V2D)

and Vehicle-to-Grid (V2G). The purpose of this research question is to identify the V2X used by the UAVs to connect to other entities. Among all these types of communication, reviewers have found references to V2I and V2V only.

The main motivations for V2X are road safety, traffic efficiency, and energy savings within smart cities. UAVs are one of the means for setting up the smart city concept [START_REF] Glaser | Smart Cities Growth Opportunities Overview 2016-2021[END_REF]. Therefore, it is important to highlight the V2X technologies that are considered within UAV models. Finally, 33% of the papers do not consider any specific communication approach. Therefore, when communication is considered within these primary studies, it is not detailed; and the authors seem to assume that the information is exchanged whatever the communication mean is. These papers are not excluded because they contain models that support interaction among the UAV agents, even if it is in an abstract level.

It is interesting to note that 66% of the papers consider that UAVs are connected entities that need to interact with their environment or with other UAVs. This is in-line with the fact that UAVs may contribute to set up the smart city concept (c.f. Section RQ4).

Family of models (RQ6)

The purpose of this RQ is to identify the family of the models that are used within the proposed works. Three major families are considered:

• Mathematical model: including formal models enable to verify and validate the behavior of the UAVs. Formal verification is the act of proving or disproving the correctness of UAV algorithms with respect to a certain formal specification, using formal methods of mathematics. The verification of these systems is done by providing a formal proof on an abstract mathematical model of the system, the correspondence between the mathematical model and the nature of the system being otherwise known by construction.

• Algorithm-based model: are the models based on the general computer programming theory. These models are basically instances of a logic written in a software to produce the UAV behaviors. These algorithms are not based on mathematical models, such that it is hard to give a proof of completeness and stability.

• Not-categorized model: If a paper's contribution can be classified neither mathematically nor as algorithm-based, it is put into the "None" category. In most of the reviewed papers, the contributions within this category are presented with abstract or general explanations without equations, algorithms, state machine, etc. For example, the UAV behavior is described by a schematic drawing.

Fig. 23 shows a repartition of the models according to their family. It is interesting to note that 38% of the proposed models are mathematical, and 43% are algorithm-based. Indeed, even if mathematical models are harder to define than algorithm-based models, safety concerns related to UAVs lead researchers to give a proof of safety and stability of the UAV behaviors over the time by providing mathematical models. Safety validation is not outside the algorithm-based models. In all the related papers, simulation testbeds are used for validating the behaviors of the proposed models.

As mentioned within the RQ3 analysis, the architecture in the reviewed models could be classified as centralized or decentralized architectures. Fig. 24 shows the correlation between the system architecture classification and the mathematical/algorithm-based model classification. As expected for MAS, the architectures are mostly decentralized, whatever the type of model.

Evaluation and simulation scenarios (RQ7)

The purpose of this research question is related to the evaluation of the proposed models: whether it relies on a dataset, or on a generated synthetic dataset, or no dataset. Only a few of the reviewed papers (5%) have a reference to a dataset for setting up the UAV simulations; 17% of the papers have generated a specific dataset for evaluation; While, the majority have no dataset.

In papers with no datasets, simulation scenarios are defined as ad hoc by the authors.

This relatively low number of papers with datasets may be explained by the difficulties for building such sets, e.g. it is hard to gather realistic data and initialize a UAV model from it. This issue is related to the research direction RD7 detailed below. Having well-established testbeds with datasets helps to unify the testing process, and allows for systematic comparisons between the solutions proposed by researchers.

Simulation Frameworks (RQ8)

Enabling early validation of a UAV system design requires the simulation of its components. This requires the development of adapted simulation environment. Identifying the frameworks used to implement the proposed solutions, and the main advantages & disadvantages of each of them is a challenge by itself.

Fig. 27 shows the used simulation frameworks in the reviewed papers. Several frameworks are used: AgentFly [START_REF] Sislak | Multi-Agent Simulation of En-Route Human Air-Traffic Controller[END_REF], Simulink [START_REF] Ozpineci | Simulink implementation of induction machine model-a modular approach[END_REF], Gazebo [START_REF] Koenig | Design and use paradigms for gazebo, an open-source multi-robot simulator[END_REF], NetLogo [START_REF] Wilensky | NetLogo: Center for connected learning and computerbased modeling[END_REF], MASON [START_REF] Luke | MASON: A Multiagent Simulation Environment[END_REF], A-globe [START_REF] Šišlák | A-globe: Agent platform with inaccessibility and mobility support[END_REF], Repast Simphony [START_REF] North | The repast simphony runtime system[END_REF], JADE [START_REF] Bellifemine | Developing multi-agent systems with JADE[END_REF], PROMELA [START_REF] Holzmann | The SPIN model checker: Primer and reference manual[END_REF], Gwendolen [START_REF] Dennis | Gwendolen: a BDI language for verifiable agents[END_REF], Neptus [START_REF] Dias | Neptus-a framework to support multiple vehicle operation[END_REF], jME3 [START_REF] Veloso | A platform for the design, simulation and development of quadcopter multi-agent systems[END_REF], and SPADE [START_REF] Gregori | A jabber-based multi-agent system platform[END_REF]. Table 6 provides the list of these frameworks. All used frameworks are open source except for AgentFly, A-globe and Simulink. In our previous work [START_REF] Mualla | Comparison of Agent-based Simulation Frameworks for Unmanned Aerial Transportation Applications[END_REF], a comparison between open-source ABS frameworks was provided. However, the comparison lacked some frameworks that were revealed by the SLR provided in this work. The two most used frameworks by the papers in the SLR are AgentFly (7%), Simulink (7%). However, the result indicate that no framework was favored by researchers for civilian UAV applications. The larger part of the of implementations in the reviewed papers is using an ad hoc framework (43%). These simulation frameworks are typically developed by the authors of the reviewed papers from scratch. This fact leads us to consider that the existing frameworks do not cover all the needs mandatory for implementing a UAV simulation software. Moreover, and due to the abundance of frameworks with no clear distinguished features related to UAVs, the authors generally prefer to setup their own configuration and build the simulation framework from scratch even though it is time-consuming. Regarding the public availability of the implemented solution, only one paper has provided full access to the code [START_REF] Zou | Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search[END_REF], and some papers provided partial access to pieces of code or to open source tools that they used [START_REF] Sampedro | A flexible and dynamic mission planning architecture for uav swarm coordination[END_REF][START_REF] Khaleghi | A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs[END_REF][START_REF] Vasilijevic | Heterogeneous robotic system for underwater oil spill survey[END_REF][START_REF] Ma | Decentralized prioritized motion planning for multiple autonomous UAVs in 3D polygonal obstacle environments[END_REF][START_REF] Veloso | A platform for the design, simulation and development of quadcopter multi-agent systems[END_REF][START_REF] Ciarletta | Simulation and Platform Tools to develop safe flock of UAVs: a CPS Application-Driven Research[END_REF][START_REF] Schatten | Multi-agent based Traffic Control of Autonomous Unmanned Aerial Vehicles[END_REF][START_REF] Obdržálek | Mobile agents in multi-agent UAV/UGV system[END_REF], while the rest of papers did not provide any access.

Discussion

The results and the discussion above help to understand the recent tendencies in the studied domain. Nevertheless, the conclusions drawn in this paper are only valid within the predefined domain of ABS and MAS for civilian UAV applications. Thus the tendencies discussed in Section 3 cannot be generated to all UAV applications. Their scope and validity are limited by the keywords and the exclusion criteria defined in Section 2.

Based on the quality criteria defined in Section 2.2.3, the quality of the papers were evaluated according to the four quality criteria defined in Table 1 related to the explanations on the motivations (Q1), the study context (Q2), the theoretical and experimental results (Q3), and the limitations and research directions (Q4). Fig. 26 shows the average evaluation for each of these criteria.

Reviewers have provided a score, according to their background and knowledge, based on three levels of quality: "bad", "average", "good". Each paper was evaluated by at least two reviewers and the results were averaged into a scale from 1 to 5. Over the entire set of the papers, motivations and contexts are clearly explained. Presentation of the results is described with the minimum set of details to allow a researcher to reproduce the presented results. Finally, as can be seen from the figure, reviewers have considered that the limitations of the proposed models and approaches are not enough detailed within the papers.

However, as UAV technology evolves and new UAV manufacturers claim new customers and social flight clubs enlist fresh enthusiast amateur pilots, these open issues and limitations are becoming significant challenges.

For instance, regulation and collision avoidance are among the prominent challenges to be settled. Yet, the air is still largely unregulated and unmarked, especially to the naked eye, unequipped with height measuring methods, without prior knowledge of any restrictions regarding the filming of surrounding people, and the seriousness of the threat a UAV poses as it zooms past or above people.

Moreover, enhanced availability of better GPS-trackers, quieter copters, and smaller "footprint" also raises new legal issues and requires current and upto-date regulation. Nevertheless, the vast majority of the world still remains behind on effective UAV control. Yet, these issues have not received enough attention in the reviewed papers. Namely, regulation is addressed directly in no more than 7% of the papers. while collision avoidance and UAV safety are addressed only by 11% of the papers.

To help address these understudied open issues and limitations, the next section identifies the main research directions.

Challenges and Research Directions

In this section, the major challenges and research directions related to UAVs are listed. These directions are either mentioned in the reviewed papers or identified based on our own experience. The following list provides a synthesis of these challenges and research directions.

RD1: Making the UAV autonomous; This research direction is neither specifically control-related, nor concerned with inner-loop feedback stability and control. Since the early UAV research works [START_REF] Chandler | Research Issues in Autonomous Control of Tactical UAVs[END_REF], several related issues have been considered: (i) secure communications, unjammable data link (this point is specifically addressed in RD3); (ii) coordination;

(iii) target identification; (iv) target assignment; and (v) deconfliction.

References cite automated decision aiding/decision making as one of the most difficult problems leading to autonomy. Man-machine interface, communications, task and path planning, and target identification are real and difficult issues.

The aim of a typical UAV planning algorithm is to determine a path through the environment while satisfying all the constraints and the mission objectives. Such a planning algorithm involves several levels of planning: (i) pre-mission planning where all the information is static. This is the best plan that can be made based on the available informa- UAVs, and opens the door for challenges like flexible autonomy [START_REF] Bürkle | Towards Autonomous Micro UAV Swarms[END_REF] and initiative decision-making [START_REF] De Castro | An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery[END_REF].

RD2: Explaining the UAV Behavior ; As shown in the previous section, there is a confirmed tendency towards the development of increasingly autonomous UAV systems. This evolution would minimize the human intervention by relieving the human operator from the burden of continuously monitoring the UAVs. Nevertheless, in unpredictable situations, the UAV behavior might not conform to expectations of the human operator. For instance, in a product delivery scenario, an autonomous UAV may choose to deviate from its expected path because of an unforeseen event. Enhancing the UAVs with explaining capabilities would allow the human operator to understand the reasons behind UAV behavior and raises its trust in the autonomous UAV system. Moreover, the recent developments of the domain of eXplainable AI (XAI) help UAVs to move in this direction. As a matter of fact, UAV has been cited as one of the applications where XAI would be needed [START_REF] Gunning | Explainable artificial intelligence (xai), Defense Advanced Research Projects Agency (DARPA)[END_REF]. Furthermore, developing explainable UAV would have a very positive impact on human-machine teaming. Recently, this research direction is being explored by military UAV applications. Similar efforts should be considered for civilian applications [START_REF] Van Der Vecht | SAIL: a social artificial intelligence layer for human-machine teaming[END_REF]. As shown by recent studies, using BDI agents is a promising approach to develop explainable agents [START_REF] Broekens | Do you get it? User-evaluated explainable BDI agents[END_REF].

A key explanation for this success lies in the fact that BDI paradigm is inspired from folk psychology which means that "the core concepts of the agent framework map easily to the language people use to describe their reasoning and actions in everyday conversations" [START_REF] Norling | Folk psychology for human modelling: Extending the BDI paradigm[END_REF]. Therefore, BDI architecture offers a more straightforward description making models easier to explain for end-users. For this reason, UAV agents relying on BDI architectures, which represent only 14% of papers reviewed in this SLR (c.f. Figure 16), are likely to increase in numbers if the issue of explainable UAV behavior is to become a hot research topic. This well-known problem is related to the UAV design and concerns the improvement of the UAV battery life. Currently, the battery of a typical UAV allows for up to 13 to 20 minutes of continues flight. In a large number of application cases, this duration is not long enough to enable UAVs to achieve their objective before they run out of power and fall down. There are some technical solutions allowing to increase the battery life. Yet, as the battery capacity increases, its size and weight generally increase, which cause the UAVs to consume more power. Battery management can help handle this issue by managing battery consumption prediction and planning [START_REF] Ure | An automated battery management system to enable persistent missions with multiple aerial vehicles[END_REF], scheduling [START_REF] Park | Battery assignment and scheduling for drone delivery businesses[END_REF], and replacement of battery [START_REF] Suzuki | Automatic battery replacement system for UAVs: Analysis and design[END_REF][START_REF] Lee | Autonomous battery swapping system for quadcopter[END_REF]. Battery wireless charging is also an option through power lines [START_REF] Simic | Investigation in Wireless Power Transmission for UAV Charging[END_REF], or an automatic charging system that uses charging stations positioned along the path of the UAV mission [START_REF] Wang | Design of wireless power transfer device for UAV[END_REF]. For long-endurance and high-altitude flights, solar-powered UAVs could be used [START_REF] Hosseini | Energy-aware aerial surveillance for a long-endurance solar-powered Unmanned Aerial Vehicles[END_REF][START_REF] Lee | Optimal path planning of solar-powered UAV using gravitational potential energy[END_REF][START_REF] Sa | SUAV: Q-An improved design for a transformable solarpowered UAV[END_REF].

RD5: Integrating UAVs into smart cities; A smart city integrates heterogeneous connected objects to automate or simplify autonomous and transparent accomplishment of various daily tasks, both personal and professional [START_REF] Glaser | Smart Cities Growth Opportunities Overview 2016-2021[END_REF]. A smart city is an urban area that uses different types of electronic data collection sensors to supply information which is used to manage assets and resources efficiently. This includes data collected from citizens, devices, and assets that are processed and analyzed to monitor and manage traffic and transportation systems, power plants, water supply networks, waste management, law enforcement, information systems, schools, libraries, hospitals, and other community services [START_REF] Mclaren | Sharing Cities: A Case for Truly Smart and Sustainable Cities[END_REF].

The smart city concept integrates ICT, and various physical devices connected to the network to optimize the efficiency of city operations and services and connect to citizens [START_REF] Peris-Ortiz | Sustainable Smart Cities: Creating Spaces for Technological, Social and Business Development[END_REF]. According to Farhan et al. [START_REF] Farhan | International Conference on Unmanned Aircraft Systems (ICUAS)[END_REF],

several opportunities for UAVs uses to support a smart city exist. These opportunities will be very beneficial to any smart city that would utilize UAVs for their economic growth and development.

One of the new trends in civilian UAV applications in smart cities is using Information System (GIS) applications in smart cities in addition to being helpful for environmental analysis. These opportunities may lead to cost reduction and cutting down on the number of manpower hours involved in such activities.

The integration of UAV solutions with machine-to-machine, Radiofrequency identification (RFID) and live video streaming increased the role of UAVs in public safety areas. This new trend will move the cities management personnel from being reactive to proactive and will leverage data. Furthermore, the involvement of UAVs in surveillance activities will reduce costs and increase the efficiency of the operations. In India, some work examines civilian UAV operations and analyses the major policy gaps in the country's evolving policy framework. It argues that ad hoc measures taken by agencies have been ineffective, whether in addressing issues of quality control, or response mechanisms, questions of privacy and trespass, air traffic, and legal liability [START_REF] Rajagopalan | Drones: Guidelines, regulations, and policy gaps in India[END_REF].

USA has by far the most mature civilian UAV regulations in place.

The New Small UAS Rule (107) of the Federal Aviation Administration (FAA) that was issued in August 2016 regulates most of the UAV operations, especially those related to commercial or civilian purposes.

The FAA has relaxed the regulations for UAV operations in the commercial sector considering that the UAV applications are estimated to generate an additional US$82 billion for the economy of USA.

Making progress on the issue of reaching a common legislation will be a complicated task. This is because international conventions on international civilian aviation, such as the Chicago Convention, apply only to civilian manned aircraft but not to unmanned ones [START_REF] Chamoso | The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments[END_REF]. It is necessary to have a legislation that will be open and generic in the technical aspects. This is because legislations that are limited to specific aircraft types or only permit the use of remote controls with certain characteristics would become obsolete in the near future, as new advances in the field of UAVs appear Furthermore, regulations should not only consider the civilian liability of these devices but also aspects that will assure the security of the citizens, for example, the protection of data in deployed vehicles [START_REF] Chamoso | The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments[END_REF].

RD7: Verifying and validating the UAV behavior ; According to [START_REF] Torens | Certification and Software Verification Considerations for Autonomous Unmanned Aircraft[END_REF], the more complex a UAV software function gets, the more difficult it becomes to test. Furthermore, functional requirements are only one aspect of a system. Beyond the pure verification of a requirement lies the benchmark of the implemented solution. The resulting outcome may be determined by a test of the requirement, but the specific path to the solution can have different levels of quality. Therefore, additional tests have to verify that the specified safety boundaries, as well as additional constraints, are met by this algorithm. To be able to assess highly automated functions and to be able to assure a high-quality software system, it is, therefore, necessary to implement a scoring system or a benchmark to evaluate the autonomy using non-functional requirements. For example, for path planning the standardized benchmark by [START_REF] Mettler | Benchmarking of Obstacle Field Navigation Algorithms for Autonomous Helicopters[END_REF] is utilized to measure the correctness and result quality by expressing a set of pass/fail criteria from the standardized baseline solution.

It is important to notice that benchmarks are problem specific, and not implementation specific. This enables developers not only to test a pathplanning algorithm automatically, without a manual review from an engineer, but also to evaluate algorithms and compare them with different implementations and solution approaches. The development of such automatic tests and benchmarks for additional problems, such as sensor fusion and computer vision, to generally assess the capability to fulfill navigation safety and performance requirements, will be critical to the progress of UAVs and the evaluation of new approaches [START_REF] Torens | Certification and Software Verification Considerations for Autonomous Unmanned Aircraft[END_REF].

The growing pressure to innovate and the demand for shorter development cycles require changes in the UAV development methodology. As a result, there is a shift in the demands on testbed systems. This desire for shorter development times stands opposed to the growing complexity re-quired for developing of increasingly automated and autonomous systems [START_REF] Paulweber | Powertrain Instrumentation and Test Systems: Development -Hybridization -Electrification[END_REF]. Enabling early validation of such system designs requires the simulation of components. This requires the development of adapted simulation environment, possibly real-time, composed of a collection of reusable modules combining real and virtual components (also called XiL: X-inthe-Loop, where X meaning alternatively Model/Software/Hardware and Human).

Related Works

Recently, several works surveyed the emerging topics of UAVs. However, these works mainly focused on vertical applications without considering the aspects and challenges across multiple application domains and research topics.

For instance, Hayat et al. [START_REF] Hayat | Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint[END_REF] focused on the characteristics and requirements of UAV networks for envisioned civilian applications between 2000 and 2015 from a communications and networking point of view. Motlagh et al. [START_REF] Motlagh | Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives[END_REF] reviewed Low-altitude UAVs highlighting their potential use in the delivery of IoT services from the sky. Other surveys focused on traffic management [START_REF] Puri | A survey of unmanned aerial vehicles (UAV) for traffic surveillance[END_REF],

environmental monitoring [START_REF] Bayat | Environmental monitoring using autonomous vehicles: a survey of recent searching techniques[END_REF], ad hoc networks in UAV applications [START_REF] Zhao | Topology Control and Mobility Strategy for UAV Ad-hoc Networks: A Survey[END_REF],

routing and energy efficiency in UAV communication networks [START_REF] Gupta | Survey of important issues in UAV communication networks[END_REF], and UAV coverage [START_REF] Chen | The coverage problem in UAV network: A survey[END_REF].

One interesting survey is provided by Chmaj and Selvaraj [START_REF] Chmaj | Distributed processing applications for UAV/drones: a survey[END_REF] in which the authors surveyed the applications implemented using cooperative swarms of UAVs that operate as a distributed processing system. However, this survey did not consider the challenges facing UAVs in these applications and the potential role of new technologies in UAV uses.

Shakhatreh et al. [START_REF] Shakhatreh | Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges[END_REF] reviewed civilian UAV applications and challenges.

They identify current research trends and future challenges for civilian UAV applications, including: charging, collision avoidance, swarming, networking and security-related challenges. Yet, this survey was mainly inspired by low-level aspects of the UAV like networking and wireless communication. Moreover, the listing of the comparison was in sequence without a cross-application domain discussion.

Other surveys focused on system identification and UAV-human interactions.

In particular, current methods and applications of system identification for small low-cost UAVs were provided by Hoffer et al. [START_REF] Hoffer | Small low-cost unmanned aerial vehicle system identification: a survey and categorization[END_REF], while the interaction between UAVs and humans applications were considered in another survey [START_REF] Kolling | Human interaction with robot swarms: A survey[END_REF]. In this later work, a taxonomy of control methods that enable operators to control swarms effectively was developed. With highlighting challenges, unanswered questions, and open problems for Human-Swarm interaction.

Conclusions

UAVs are becoming increasingly popular for civilian applications. The aim of this paper is twofold. First, conduct a SLR on research addressing MAS, and specifically ABS, in civilian UAV applications. Second, outline and analyze the challenging research direction of civilian UAV applications. The following section states the principal findings. Note that while the SLR concentrated on research using ABS for civilian UAV applications, some of the findings below pertain to key research issues in agent and MAS (e.g. agent architectures, decentralization, etc.).

Following a well established SLR methodology, we identified 8 Research Questions (RQ) helping to asses the contributions of MAS and ABS in civilian UAV applications. The main findings were:

1. Research on MAS and ABS for civilian UAV applications has witnessed a considerable increase in the past decade, and most of the reviewed papers were written in Europe followed by North America and Asia.

2. Coordination, mission management, UAV formation (platooning), collision avoidance, task allocation, and path planning were the most studied research topics while "Urban planning" and "General" accounted for the majority of application domains.

3. The majority of papers covered by the SLR opted for a goal-directed agent architecture to model their agents. Furthermore, most of these studies adopted a decentralized system architecture.

4. Despite the key role that the UAV is expected to assume in smart cities and connected smart environments, only a fifth of the reviewed paper integrate IoT technologies in their research works.

5. The majority of the reviewed papers address UAV connectivity. This

shows that most of the reviewed papers view UAVs as connected entities both among themselves and with their environment.

6. Algorithm-based models were used slightly more than the mathematical models by the reviewed papers.

7. To evaluate their contributions, only 5% of papers rely on public datasets and less than 20% use generated datasets. The remaining majority do not use any dataset for evaluation purposes. This underlines the absence of common testbeds and datasets allowing to evaluate and compare these works.

8. In a related finding, the results showed that to conduct their experiments, about 40% of the studied papers implement their own ad hoc simulations.

This might be an indication that existing frameworks are not meeting all the needs required for implementing UAV simulations.

As a result of this analysis, we outlined the following key challenges and research Regarding the SLR, a future work will be to compare the contributions based on MAS to those using real UAVs. In this way, it will be possible to highlight the position and the growth of MAS within the UAV community.

Additionally, RD7 and the need for simulation testbeds suggest that the use of ABS frameworks for UAVs will gain more interest in complex civilian applications scenarios. Therefore, one future work is to rely on the results of this SLR to select specific criteria allowing to define a methodology to compare ABS frameworks and evaluate them based on civilian UAV application considerations.

Figure 1 :

 1 Figure 1: The systematic literature review process (the figure adapted from [18, 19]).

RQ3. 1 :RQ3. 2 :RQ3. 3 :

 123 Investigate the agent architecture used in the solution (e.g. cognitive agent, Belief-Desire-Intension (BDI) agent, reactive agent, etc.). Investigate the architecture of the system in the studied papers (decentralized vs centralized). Investigate whether the proposed model (agent or simulation architectures) includes the environment, and how the UAVs interact with their environment.RQ4: Investigate whether models and technologies enabling to implement concepts related to Internet-of-Things (IoT), pervasive systems or ubiquitous systems are considered in the studied papers. IoT is a technological domain that is more and more used within smart cities. This RQ will highlight if technologies like wireless sensor networks, connected vehicles, connected buildings, etc. are considered as a component of the agentbased system in the studied papers. In such systems, IoT contributes to the model at the agent level (objects may be modeled as agents) and at the agent environment level (objects are not agents). This RQ will also highlight how IoT devices and UAVs are interacting together. RQ5: Identify the communication technology used by the UAVs to connect to other entities: In order for UAVs to be deployed and used in their environment, especially in smart cities, they could either be connected to infrastructure entities, i.e. Vehicle-to-Infrastructure (V2I), or to other UAVs, i.e. Vehicle-to-Vehicle (V2V). Understanding the communication technology of the UAV in the proposed simulated works is key to assess whether frameworks are capable of producing realistic simulations. RQ6: Identify the main model category used by the proposed work (e.g. mathematical, algorithm-based, etc.) This RQ is set up in order to determine if the contributions to civilian UAV applications are formal or semi-formal. It will lead us to a statement and arguments regarding the validation of UAV models. RQ7: Assess the evaluation of the proposed model: Simulation for civilian UAV applications needs scenarios to be set up, and the use of datasets may help to create such simulation in a realistic way. This RQ assesses if the evaluation relies on a dataset, on a generated synthetic dataset, or no dataset. This will highlight the different datasets, and the scenarios (if no dataset is used) from literature. RQ8: Identify the simulation frameworks used to implement the proposed solutions, and the main advantages & disadvantages of each framework especially if it excels in a specific civilian UAV application domain. This RQ is related to the technological means used by the researchers for implementing the MAS for civilian UAV applications. It should help researchers to determine and choose the best framework for their own model implementation.

  apply a set of exclusion criteria to retain only pertinent articles. The set of exclusion criteria, defined by the authors, is listed below: ExC1: Not a recent research work -Papers that were published before 2008, i.e. with a publication year < 2008, are excluded. It is assumed that the non-recent research is not up-to-date due to the high evolution rate of the UAV technologies and usages.

Fig. 2

 2 Fig.2plots the geographical distributions of papers before the fine-grained inclusion/exclusion step. The number of papers published by US researchers is highest worldwide. The geographical distribution of the papers could be partly explained by the investment rate in R&D in each country[START_REF]How much does your country invest in R&D?[END_REF], illustrated by Fig.3. The notable exception is China, which invests 2% of its GDP (U S$370, 589.7M) but has a number of papers equal to France (2.3% of GPP, US$60, 781.6M). Another notable point is the Czech Rep., which has 11 papers with an average R&D investment (2% of GPP, US$6, 719M). All the authors from Czech Rep. are collaborating with partners within US-funded projects.

10

 10

Figure 2 :Figure 3 :

 23 Figure 2:The geographical distribution of papers after the coarse-grained exclusion/inclusion step.

Figure 4 :Figure 5 :Figure 6 :

 456 Figure 4: The number of papers per year after the coarse-grained exclusion/inclusion step.

Figure 7 :

 7 Figure 7: The number of papers with 2D and 3D simulation scenarios per year.

Fig. 6

 6 compares the percentage of papers presenting 2D and 3D simulations in the pool of papers before the fine-grained exclusion/inclusion (whose total number is 123), excluding 26 papers that were not determined either they use simulation or not, or either they are 2D or 3D. As can be seen from the figure, despite ExC13 being the most common cause for exclusion, the majority of the papers whose simulation dimension was determined ended up addressing 3D simulations. In order to understand the general tendencies of 2D and 3D simulations, Fig.7plots the number of papers proposing 2D/3D simulations per year. As shown in the figure, the number of papers proposing 3D

(

  Fig. 4, the number of papers witnesses a roughly stable growth between 2008 and 2016. To understand the geographic distributions of the main contributors in the studied domain, Fig. 9 plots the number of papers per country after the finegrained exclusion/inclusion step. Compared with Fig. 2, The number of papers published by US researchers is still highest worldwide. As previously noticed, researchers from the Czech Rep. are collaborating with partners within US-

Figure 8 :

 8 Figure 8: The number of papers per year after the fine-grained exclusion/inclusion step.

Figure 9 :Figure 10 :

 910 Figure 9: The geographical distribution of papers after the fine-grained exclusion/inclusion step.

Figure 13 :

 13 Figure 13: The main civilian UAV application domains.

T4:

  UAV Formation (Platooning) (9%) addresses the definition of flight formations for UAVs; T5: Surveillance (9%) enables UAVs to help the detection of dangerous and illegal situations; T6: Path Planning (7%) focuses on the statical and dynamical computing of the best paths to fly along according to the environmental constraints; T7: Task Allocation (7%) is the optimal dynamic (potentially distributed) assignment of tasks to the UAVs; T8: Certification & Regulation (7%) is related to the definition of regulations dedicated to UAVs, and of the associated certifications for UAVs or pilots; T9: Simulation (7%) focuses on the design and implementation of developed simulation frameworks to understand and validate UAV behaviors; T10: Communication (3%) is related to the definitions of the means of communication between the UAVs, and between a UAVs and the ground infrastructure; T11: Coverage (3%) addresses the problems of map coverage; T12: Tracking (3%) focuses on the detection and tracking of objects in the environment of the UAVs.

  domain. The resulting application domains are: D1: General (53%); D2: Urban Planning (26%); D3: Emergency Response (12%); D4: Telecommunication (5%); D5: Agriculture (2%); D6: Border surveillance (2%). As seen in Figure Fig. 13, Urban planning and General are the most common application domains. This figure shows the growing attention given to UAV applications in civilian and urban environments since the share of urban planning is about 26%, while other application domains such as agriculture, emergency response, border surveillance, which often take place outside the urban environment, received less attention.

Fig. 14 shows

 14 Fig. 14 shows the average Q3 score obtained per application domain. As can be seen from the figure, based on their maturity, the application domains can be classified into two clusters. The first cluster represents relatively mature application domains (agriculture, telecommunications, and emergency response).

Figure 14 :

 14 Figure 14: The average of quality metric Q3 given by reviewers per application domain.

Figure 15 :Figure 16 :Figure 17 :Figure 18 :Figure 19 :

 1516171819 Figure 15: The average ACL per agent architecture.

Figure 20 :

 20 Figure 20: The number of papers with agent architecture as per research topic.

Fig. 20 shows

 20 Fig. 20 shows the number of papers with agent architecture as per research topic. The most used agent architectures are cognitive (without BDI) (17) and reactive (without flocking) (9), considering all the research topics. This fact is explained by the characteristics of the research topics: the ideal agent-based modeling of UAVs is usually based on a multi-layer architecture [67], illustrated by Fig. 17. The operational layer corresponds to the (very-

  . The operational layer corresponds to the (very-)short term, i.e. the control of the UAVs. The tactic layer is associated with the planning of the UAV actions, e.g. path planning. The strategic layer is associated with the missions of the UAVs. In this layer, mission and task management need more complex models typically found in the cognitive scope. It is interesting to note that a low number of papers contains a multi-layer model.

Figure 21 :Figure 22 :Figure 23 :Figure 24 :Figure 25 :Figure 26 :

 212223242526 Figure 21: The proportion of the models that are including or not the IoT concept.

Fig. 19 shows

 19 Fig.19shows the proportion of the dynamic and static agent environments.Half of the papers propose models based on a static environment, 48% on a dynamic environment. Due to the complexity of the UAV systems, static agent environments are used in order to control the complexity of the modeling and enabling an easier validation.

Fig. 22 plots

 22 Fig. 22 plots the type of communications used: 33% of the papers includes a communication between the UAVs and the infrastructure (V2I), 17% between UAVs (V2V) by using: (i) implicit communication, i.e. the communication means are not explicitly described; (ii) direct communication means, e.g. Wireless; or (iii) by using stigmergy communication. 17% of the papers propose a model with both V2I and V2V components.

Figure 27 :

 27 Figure 27: The used simulation frameworks.

  tion. (ii) near term plan, which is executed in the air and involves the physical flying of the UAVs. This can be looked at as a trajectory tracking task, which can be completely automated through inner-loop control, autopilot design, tracking, and short-term on-line trajectory generation.It is the intermediate level of planning where the desired capability of autonomous control is achieved. (iii) Near real-time in-flight replanning is needed as new sensor information or commands are received by the UAVs. The challenge is to optimally update the off-line plan as per the new received information and/or the unforeseen occurred events. The optimization problem is dominated by size, complexity, uncertainty, and the fact that the mission could be shared by different UAVs. This leads to problems related to the cooperation and coordination of the autonomous

RD3:

  Ensuring Security and Authentication; The quest of developing autonomous and decentralized UAV systems raises several challenges related to security and authentication. For instance, within a swarm, a UAV must be able to detect and entrust its peers, and ensure data confidentiality and integrity. Otherwise, faulty or malicious UAVs may deviate the swarm behavior[START_REF] Ferrer | The blockchain: a new framework for robotic swarm systems[END_REF]. These challenges are accentuated by the fact that most UAV swarms are designed to operate autonomously without a central regulating authority. Recent research has outlined Block-Chain Technology (BCT) as a potential solution to overcome this problem. In addition to providing a platform allowing for secured interactions among UAVs, BCT can also be used to enhance the decentralized decision making of the UAV swarm since it allows all the participants to share an identical view of the world, and facilitate agreements among UAV teams[START_REF] Ferrer | The blockchain: a new framework for robotic swarm systems[END_REF]. Yet, before BCT is ready to be used with UAVs, several limitations must be solved[START_REF] Ferrer | The blockchain: a new framework for robotic swarm systems[END_REF]: (i) latency: the time required to update the blockchain is too long to allow real-time compliance (a common requirement in UAV applications); (ii) Most of the UAVs are resource-constrained devices which might be unable to undertake the heavy computations demanded by BCT. For this reason, recent research works in BCT propose lightweight techniques (e.g. "proof of stake") that can be handled by resource-constrained devices such as the UAVs; (iii) On the long run, BCT can be very demanding in terms of bandwidth and communication overhead since all agents should share a copy of a long "ledger" of interactions[START_REF] Wagner | Ensuring network scalibility: How to fight blockchain bloat[END_REF]. This issue, known as "bloat", poses a considerable challenge to UAV teams which have limited hardware and communication capabilities.RD4: Increasing the flight duration; Another problem is the UAV battery life.

  UAVs in geospatial surveying. The main design of a smart city requires the optimization of data flows provided by wireless sensor networks as sensors are the main component of any autonomous system such as those involving UAVs. This combination of technologies creates a wide range of applications and opportunities such as fire management in open areas where the use of UAVs and micro-UAVs is very beneficial. The potentials vary from a wide range of available solutions and innovations that are evolving quickly. Due to the reliability of most UAV designs, integration of such technologies make it possible to install wireless sensors on-board to make the UAVs usable in geospatial, land surveying and Geographic

  directions: (i) Model design of fully autonomous UAV; (ii) Explanation of the UAV behaviors when they are part of a complex system; (iii) Security and authentication of UAV; (iv) Increasing of the flight duration; (v) Technical integration of UAV into smart cities; (vi) Proposal and evaluation of UAVrelated regulations; and (vii) Verification and validation the UAV behaviors.

Table 1 :

 1

		The Quality Questions
	#	Quality Question
	Q1	Do the authors provide a sound rationale (i.e. motivation) for their work?
	Q2

Table 2 :

 2 The results of the coarse-grained exclusion/inclusion step

	Database	Number of Papers	Percentage
	Selected from Keyword Set 1	131	≈ 41%
	Selected from Keyword Set 2	185	≈ 59%
	Sum from Set 1 & 2	316	=100%
	Total Included	123	≈ 39%
	Total Excluded	193	≈ 61%

Table 3 :

 3 The results of the fine-grained exclusion/inclusion step

		Number of Papers	Percentage
	Included	30	≈ 24%
	Excluded	70	≈ 57%
	Conflict	23	≈ 19%
	Referee Included	12	≈ 10%
	Total Included	42	≈ 34%
	Total Excluded	81	≈ 66%

  Table 4 lists the papers per each research topic. It is worth mentioning that some papers were flagged as having more than one research topic. Therefore, and to normalize the weights given by each paper to the research topics distribution, we have decided to assign the most dominant research topic per paper. The addressed research topics are:

	T1: Coordination (17%), UAVs interact in order to coordinate their actions for
	reaching their common objectives;

T2: Mission Management (14%) addresses the optimal dynamic assignment of high-level missions, i.e. objectives, to UAVs. High-level missions are those where the UAVs rely on a high-level description of their objectives without many details and without human guidance; T3: Collision Avoidance (14%) allows to increase UAV safety by avoiding collisions among UAVs, persons, animals, and other objects;

Table 4 :

 4 The reviewed papers per research topicVan der Walle et al. [43], Sutton et al. [44], Benedetti et al. [45], Bürkle and Leuchter [46] Surveillance Semsch et al. [47], Khaleghi et al. [48], Bentz and Panagou [49], da Silva et al. [50] Task Allocation Wei et al. [51], Evertsz et al. [52], Vasilijevic et al. [6] Path Planning Volf et al. [53], Peng et al. [54], Ma et al. [55] Certification & Regulation Webster et al. [56, 57], Schatten [58] Simulation Pechoucek et al. [59], Veloso et al. [60], De Benedetti et al.

	Research topic	Papers per topic
	Coordination	Bürkle et al. [24], Zhu et al. [25], Rollo et al. [26],
		De Benedetti et al. [27], Cimino et al. [28], Ciarletta et al.
		[29], Obdržálek [30]
	Mission Management	Wei et al. [31], Gunetti et al. [32, 33], Stenger et al. [34],
		Sampedro et al. [35], Fulford et al. [36]
	Collision Avoidance	Ashraf et al. [37], Kandil et al. [38], Šišlák et al.
		[39], Arokiasami et al. [40], Zou et al. [41], Kucherov and
		Kucherov [42]
	UAV Formation (Platooning)
		[61]
	Communication	Agogino et al. [62]
	Coverage	Albani et al. [63]
	Tracking	Ferrag et al. [64]

Table 5 :

 5 The reviewed papers per application domain Application domain Papers per application domain General Ashraf et al. [37], Wei et al. [51], Bürkle et al. [24], Wei et al. [31], Rollo et al. [26], Gunetti et al. [32, 33], Evertsz et al. [52], Webster et al. [57], Sampedro et al. [35], Sutton et al. [44], Peng et al. [54], Zou et al. [41], Ma et al. [55], Bentz and Panagou [49], da Silva et al. [50], De Benedetti et al. [27, 61], Obdržálek [30], Fulford et al. [36], Kucherov and Kucherov [42], Ferrag et al. [64] Urban Planning Semsch et al. [47], Volf et al. [53], Webster et al. [56], Kandil et al. [38], Arokiasami et al. [40], Pechoucek et al. [59], Veloso et al. [60], Šišlák et al. [39], Khaleghi et al. [48], Bürkle and Leuchter [46], Schatten [58] Emergency Response Zhu et al. [25], Van der Walle et al. [43], Benedetti et al. [45], Cimino et al. [28], Vasilijevic et al. [6] Telecommunication Agogino et al. [62], Ciarletta et al. [29]

	Border Surveillance		Stenger et al. [34]
	Agriculture			Albani et al. [63]
	Task Allocation	Coordination	Collision Avoidance	Surveillance	Path Planning	Mission Management	UAV Formation (Platooning)	Coverage	Communication Certification & Regulation	Simulation	Tracking

Table 6 :

 6 The reviewed papers per used framework

	Framework	Papers using the framework
	Ad hoc	Agogino et al. [62], Ashraf et al. [37], Wei et al. [51, 31], Bürkle
		et al. [24], Rollo et al. [26], Gunetti et al. [33], Evertsz et al.
		[52], Kandil et al. [38], Sampedro et al. [35], Peng et al. [54],
		Benedetti et al. [45], De Benedetti et al. [27, 61], Bürkle and
		Leuchter [46], da Silva et al. [50], Schatten [58]
	MASON [79]	Albani et al. [63], Zou et al. [41]
	AgentFly [76]	Semsch et al. [47], Pechoucek et al. [59], Šišlák et al. [39]
	PROMELA [83]	Webster et al. [56]
	A-globe [80]	Volf et al. [53], Stenger et al. [34]
	NetLogo [78]	Cimino et al. [28], Zhu et al. [25]
	Simulink [72]	Gunetti et al. [32], Ciarletta et al. [29], Kucherov and Kucherov
		[42]
	Gwendolen [84]	Webster et al. [57]
	Gazebo [77]	Arokiasami et al. [40], Ma et al. [55]
	Repast Simphony [81]	Khaleghi et al. [48]
	Neptus [85]	Vasilijevic et al. [6]
	jME3 [60]	Veloso et al. [60]
	SPADE [86]	Obdržálek [30]
	JADE [82]	Fulford et al. [36]
	n/a

Van

der Walle et al.

[START_REF] Van Der Walle | Non-hierarchical UAV formation control for surveillance tasks[END_REF]

, Sutton et al.

[START_REF] Sutton | Hierarchical uav formation control for cooperative surveillance[END_REF]

, Bentz and Panagou

[START_REF] Bentz | 3D dynamic coverage and avoidance control in power-constrained UAV surveillance networks[END_REF]

, Ferrag et al.

[START_REF] Ferrag | Robust coordinated motion planning for UGV/UAV agents in disturbed environment[END_REF]

This term has been introduced by the French biologist Pierre-Paul Grassé in 1959 for describing the termite behavior. It is defined as: "Stimulation of the workers by the work that they performed." This term expresses the notion that the actions of an agent leave signs in the environment. These signs are perceived by itself and other agents, which determine their next actions[START_REF] Parunak | Making swarming happen[END_REF]

Acknowledgements

This work is supported by the Regional Council of Bourgogne Franche-Comté (RBFC, France) within the project UrbanFly 20174-06234/06242. This work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.