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Intermittent random denial‐of‐service attacks (IRDoS) and deception attacks in multi‐UAV systems (MUAVs) can present significant security challenges. The intrusions of IRDoS attacks and deception attacks, respectively, can interrupt the network communication among followers and manipulate the received neighbor information with a certain probability, failing MUAVs to complete the formation task. A secure formation controller is developed for MUAVs in the presence of IRDoS and deception attacks, utilizing a distributed dynamic event‐triggered mechanism (DETM). Unlike the static event‐triggered mechanism, the triggering threshold of the DETM is adaptively adjustable, which can reduce data transmission and save network resources. The stability of the system is analyzed, and sufficient conditions are derived. Additionally, the duration and probability of successful attacks are examined. Ultimately, the simulation results showcase the efficacy and superiority of the suggested approach. [ABSTRACT FROM AUTHOR]

This paper presents the development and implementation of a multiple unmanned aerial vehicle system focused on coverage path planning on multiple separated areas capable of re‐planning the collective mission in case of unexpected events. For this purpose, we present a distributed‐centralized architecture that uses heuristic and computationally efficient methods to perform the planning/re‐planning and decision‐making tasks during the control of the mission execution. We performed a computational evaluation of the algorithms, comparing them with other proposals, together with experiments in simulated and real flights. The results show that the system can distribute tasks equitably among the aircraft in an efficient way, even in the middle of the flight, when facing unexpected events; and show a higher computational efficiency when compared to multiple proposals in the state of the art. [ABSTRACT FROM AUTHOR]

The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collaboratively offers significant advantages in terms of enhanced coverage, redundancy, and operational efficiency. However, as UAV missions become more complex and operate in dynamic environments, the task assignment problem becomes increasingly challenging. Multi-UAV dynamic task assignment is critical for optimizing mission success. It involves allocating tasks to UAVs in real-time while adapting to unpredictable changes, such as sudden task appearances, UAV failures, and varying mission requirements. A key contribution of this article is that it provides a comprehensive study of state-of-the-art solutions for dynamic task assignment in multi-UAV systems from 2013 to 2024. It also introduces a comparative framework to evaluate algorithms based on metrics such as responsiveness, robustness, and scalability in handling real-world dynamic conditions. Our analysis reveals distinct strengths and limitations across three major approaches: market-based, intelligent optimization, and clustering-based solutions. Market-based solutions excel in distributed coordination and real-time adaptability, but face challenges with communication overhead. Intelligent optimization solutions, including evolutionary and swarm intelligence, provide high flexibility and performance in complex scenarios but require significant computational resources. Clustering-based solutions efficiently group and allocate tasks geographically, reducing overlap and improving efficiency, although they struggle with adaptability in dynamic environments. By identifying these strengths, limitations, and emerging trends, this article not only offers a detailed comparative analysis but also highlights critical research gaps. Specifically, it underscores the need for scalable algorithms that can efficiently handle larger UAV fleets, robust methods to adapt to sudden task changes and UAV failures, and multi-objective optimization frameworks to balance competing goals such as energy efficiency and task completion. These insights serve as a guide for future research and a valuable resource for developing resilient and efficient strategies for multi-UAV dynamic task assignment in complex environments.

In practice, the consensus performance of a multi-UAV system can degrade significantly due to the presence of measurement noise and disturbances. However, simultaneously rejecting the noise and disturbances to achieve high-precision consensus tracking control is rather challenging. In this paper, to address this issue, we propose a novel distributed consensus tracking control framework consisting of a distributed observer and a local dual-estimator-based tracking controller. Each UAV’s distributed observer estimates the leader’s states and generates the local reference, functioning even under a switching communication topology. In the local tracking controller design, we reveal that classic uncertainty and disturbance estimator (UDE)-based control can magnify the noise. By combining the measurement error estimator (MEE) with UDE, a local robust tracking controller is designed to reject noise and disturbances simultaneously. The parameter tuning of MEE and UDE is unified into a single parameter, and the monotonic relationship between this parameter and system performance is revealed by the singular perturbation theorem. Finally, the validity of the proposed control framework is verified by both simulation and comparative real-world experiments.

In this work, we present an open-source unmanned aerial vehicle (UAV) platform for research in swarm robotics. In swarm robotics, groups of robots collaborate using local interactions and collectively solve tasks beyond an individual robot’s capabilities. Individual robots must have onboard processing, communication, and sensing capabilities to autonomously react to their neighbors and immediate environment. Most research involving UAVs in swarm robotics presents only simulation results, while key landmark studies with real UAV swarms have used UAV platforms that were custom-built for the respective study. One important reason for this is that no commercial UAV platform comes pre-equipped with the ability to identify and track the positions and poses of nearby drones using only onboard sensors and computation, and in research platforms, the relevant sensing technologies are currently under development. Our aim is to provide a platform that allows swarm robotics researchers to test their algorithms on real UAVs, without having to develop their own custom-built UAVs or to wait until more advanced sensing technology is ready off-the-shelf. We provide a well-documented, entirely open-source UAV platform— S-drone (Swarm-drone)—to foster and support UAV swarm research in a laboratory environment. The S-drone uses fiducial markers in the environment and cooperative feature-level sensor fusion for inter-robot tracking to track the presence, identity, relative 2D position, and relative 2D orientation of neighboring peers. The S-drone is suitable for a wide range of contexts, supports quad-camera vision-based navigation and a variety of onboard sensing, and is extensible. It is especially suited for swarm robotics research because it can operate using strictly onboard processing and sensing without the need for global positioning systems, motion capture systems, or ground stations for off-board sensing.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions.

Accounting for wind perturbations while controlling a formation of unmanned aerial vehicles (UAVs) is of a particular interest for researchers: it helps confirm the operability of algorithms in realistic non-laboratory conditions and increases the efficiency of these algorithms. This approach, however, becomes a problem for a decentralized formation due to its interconnected structure. This research work focuses on the problem of accounting for wind perturbations for a fixed-wing UAV formation controlled using the vector field path following method in cooperative or collective circumnavigation. We only study the control based on the local data about nearby UAVs as we consider the consensus-based control. The suggested solution is an adaptive control algorithm studied with Lyapunov functions. At the same time, we conduct computer simulation using complete non-linear UAV models, as well as the comparison with an algorithm that does not account for wind perturbations. As a result, we theoretically demonstrate uniform ultimate boundedness (UUB) of the trajectories of the system in question. Simulation shows that, although the algorithm that does not account for wind perturbations can control a formation with reduced accuracy, the suggested algorithm can improve its efficiency. Therefore, the solution considered in this article helps develop the applicability of consensus-based UAV formation control methods under more realistic conditions and wind impacts. [ABSTRACT FROM AUTHOR]

This paper introduces a novel optimal robust formation method for quadcopter multiple unmanned aerial vehicle (multi-UAV) systems. Firstly, a reinforcement learning (RL) algorithm based on a unique gradient descent training approach is proposed to solve the Hamilton–Jacobi–Bellman (HJB) equation, which can effectively eliminate the requirement of the Persistent Excitation (PE) condition. Secondly, the robustness of the controlled system is emphasized, and an Uncertainty and Disturbance Estimator (UDE) observer is developed to suppress model uncertainty and external disturbances through filtering techniques. Furthermore, a switched sliding mode control technique according to the average dwell time (ADT) is employed to convert switching communication topology between UAVs dynamically, and the stability analysis of the corresponding closed-loop control systems is then performed by the use of Lyapunov analysis. Finally, the simulation examples are provided to verify the effectiveness of the designed control strategy. [ABSTRACT FROM AUTHOR]

Multiple unmanned aerial vehicles (Multi-UAV) systems have recently demonstrated significant advantages in some real-world scenarios, but the limited communication range of UAVs poses great challenges to multi-UAV collaborative decision-making. By constructing the multi-UAV cooperation problem as a multi-agent system (MAS), the cooperative decision-making among UAVs can be realized by using multi-agent reinforcement learning (MARL). Following this paradigm, this work focuses on developing partially observable MARL models that capture important information from local observations in order to select effective actions. Previous related studies employ either probability distributions or weighted mean field to update the average actions of neighborhood agents. However, they do not fully consider the feature information of surrounding neighbors, resulting in a local optimum often. In this paper, we propose a novel partially multi-agent reinforcement learning algorithm to remedy this flaw, which is based on graph attention network and partially observable mean field and is named as the GPMF algorithm for short. GPMF uses a graph attention module and a mean field module to describe how an agent is influenced by the actions of other agents at each time step. The graph attention module consists of a graph attention encoder and a differentiable attention mechanism, outputting a dynamic graph to represent the effectiveness of neighborhood agents against central agents. The mean field module approximates the effect of a neighborhood agent on a central agent as the average effect of effective neighborhood agents. Aiming at the typical task scenario of large-scale multi-UAV cooperative roundup, the proposed algorithm is evaluated based on the MAgent framework. Experimental results show that GPMF outperforms baselines including state-of-the-art partially observable mean field reinforcement learning algorithms, providing technical support for large-scale multi-UAV coordination and confrontation tasks in communication-constrained environments.

Uncrewed aerial vehicles (UAVs), also known as drones, are ubiquitous and their use cases extend today from governmental applications to civil applications such as the agricultural, medical, and transport sectors, etc. In accordance with the requirements in terms of demand, it is possible to carry out various missions involving several types of UAVs as well as various onboard sensors. According to the complexity of the mission, some configurations are required both in terms of hardware and software. This task becomes even more complex when the system is composed of autonomous UAVs that collaborate with each other without the assistance of an operator. Several factors must be considered, such as the complexity of the mission, the types of UAVs, the communication architecture, the routing protocol, the coordination of tasks, and many other factors related to the environment. Unfortunately, although there are many research works that address the use cases of multi-UAV systems, there is a gap in the literature regarding the difficulties involved with the implementation of these systems from scratch. This review article seeks to examine and understand the communication issues related to the implementation from scratch of autonomous multi-UAV systems for collaborative decisions. The manuscript will also provide a formal definition of the ecosystem of a multi-UAV system, as well as a comparative study of UAV types and related works that highlight the use cases of multi-UAV systems. In addition to the mathematical modeling of the collaborative target detection problem in distributed environments, this article establishes a comparative study of communication architectures and routing protocols in a UAV network. After reading this review paper, readers will benefit from the multicriteria decision-making roadmaps to choose the right architectures and routing protocols adapted for specific missions. The open challenges and future directions described in this manuscript can be used to understand the current limitations and how to overcome them to effectively exploit autonomous swarms in future trends.

This work demonstrates distributed motion planning for multi-rotor unmanned aerial vehicle in a windy outdoor environment. The motion planning is modeled as a receding horizon mixed integer nonlinear programming (RH-MINLP) problem. Each quadrotor solves an RH-MINLP to generate its time-optimal speed profile along a minimum snap spline path while satisfying constraints on kinematics, dynamics, communication connectivity, and collision avoidance. The presence of wind disturbances causes the motion planner to continuously regenerate new motion plans, thereby significantly increasing the computational time and possibly leading to safety violations. Control Barrier Functions (CBFs) are used for assist in collision avoidance in the face of wind disturbances while alleviating the need to recalculate the motion plans continually. The RH-MINLPs are solved using a novel combination of heuristic and optimal methods, namely Simulated Annealing and interior-point methods, respectively, to handle discrete variables and nonlinearities in real-time feasibly. The framework is validated in simulations featuring up to 50 quadrotors and Hardware-in-the-loop (HWIL) experiments, followed by outdoor field tests featuring up to 6 DJI M100 quadrotors. Results demonstrate (1) fast online motion planning for outdoor communication-centric multi-quadrotor operations and (2) the utility of CBFs in providing effective motion plans.

Ultra-wideband technology has emerged in recent years as a robust solution for localization in GNSS denied environments. In particular, its high accuracy when compared to other wireless localization solutions is enabling a wider range of collaborative and multi-robot application scenarios, being able to replace more complex and expensive motion-capture areas for use cases where accuracy in the order of tens of centimeters is sufficient. We present the first survey of UWB-based localization focused on multi-UAV systems and heterogeneous multi-robot systems. We have found that previous literature reviews do not consider in-depth the challenges in both aerial navigation and navigation with multiple robots, but also in terms of heterogeneous multi-robot systems. In particular, this is, to the best of our knowledge, the first survey to review recent advances in UWB-based (i) methods that enable ad-hoc and dynamic deployments; (ii) collaborative localization techniques; and (iii) cooperative sensing and cooperative maneuvers such as UAV docking on mobile platforms. Finally, we also review existing datasets and discuss the potential of this technology for both localization in GNSS-denied environments and collaboration in multi-robot systems. [ABSTRACT FROM AUTHOR]

Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.

Developing complex robotic systems requires expensive and time-consuming verification and testing which, especially in a case of multi-robot unmanned aerial systems (UASs), aggregates risk of hardware failures and may pose legal issues in experiments where operating more than one unmanned aircraft simultaneously is required. Thus, it is highly favorable to find and resolve most of the eventual design flaws and system bugs in a simulation, where their impacts are significantly lower. On the other hand, as the system development process approaches the final stages, the fidelity of the simulation needs to rise. However, since some phenomena that can significantly influence the system behavior are difficult to be modeled precisely, a partial embodiment of the simulation in the physical world is necessary. In this paper, we present a method for incremental development of complex unmanned aerial systems with the help of mixed reality simulations. The presented methodology is accompanied with a cost analysis to further show its benefits. The generality and versatility of the method is demonstrated in three practical use cases of various aviation systems development: (i) an unmanned system consisting of heterogeneous team of autonomous unmanned aircraft; (ii) a system for verification of collision avoidance methods among fixed wing unmanned aerial vehicles; and (iii) a system for planning collision-free paths for light-sport aircraft. [ABSTRACT FROM AUTHOR]

Multi-UAV Operations are an area of great interest in government, industry, and research community. In multi-UAV operations, a group of unmanned aerial vehicles (UAVs) are deployed to carry out missions such as search and rescue, or disaster relief. This paper discusses multi-UAV system in the face of disruptions from a system-of-systems perspective. Each UAV, with some decision-making capability, is assigned a task to perform within a multi-UAV system. When these vehicles operate in an open operational environment, the ability to cope with disruptive events becomes especially important. In other words, multi-UAV system needs to be resilient. A methodological framework to dynamically evaluate resilience alternatives during mission operation is discussed in this paper. This framework allows system to select appropriate alternative given the current context. Simulation results show that such framework can be created and used dynamically to evaluate resilient alternatives for multi-UAV system. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of optimal formation tracking control for multi-UAV systems with model uncertainty and external disturbances. Firstly, by combining the sliding mode method and a neural network, an adaptive sliding mode controller is derived that counteracts the effects of modeling uncertainty and external disturbance. Subsequently, the optimal formation tracking control problem of the original system is then converted to the optimal control problem of a nominal system, and an actor–critic reinforcement learning framework is built using adaptive neural network identifiers to recursively approximate the total optimal policy and cost function. The Lyapunov analysis method shows that the stability of the closed-loop system and the convergence of the estimation weights for the actor–critic network are guaranteed. Additionally, a formation tracking using virtual experiment platform for multi-UAV systems are constructed based on the Robot Operating System (ROS) and Gazebo simulator. Finally, virtual-reality experiments is performed to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Multiple unmanned aerial vehicles (multi-UAV) systems have been applied in many scenes to improve the flexibility and effectiveness of specific tasks. To reduce dependence on wireless communication and enhance stability during cooperative control of systems, a vision-only and universal method for relative distance calculating is presented in this paper. First, key components of the UAV were detected by a two-step method. Then, a novel feature encoding method was presented to establish the 2D-3D correspondence with the known 3D structure of UAVs and solve the Perspective-n-Point (PnP) problem. To mitigate misdetections in air-to-air scenarios, a robust auto-weighting Levenberg-Marquardt (AWLM) algorithm was integrated into pose estimation. Flight experiments of a two-UAV system in wireless-denied environments have been conducted to verify the performance of the proposed approach. The results show that the calculation error is less than 8 meters at a relative distance of up to 160 meters at the speed of 45.94 ms per frame, which means the highest precision and fastest processing speed among several similar methods. Besides, the AWLM algorithm exhibits superiority over other optimization methods with fewer outliers (below 0.67%) and a smaller error bound (2.899 meters). [ABSTRACT FROM AUTHOR]

In this work, we aim to model the multi-UAV rendezvous recharging problem , which consists of energy-limited aerial vehicles that rendezvous with a mobile or fixed charging station. The motivation for such a problem is to tackle persistent surveillance missions, where the modeling of related problems often rely on heavy mathematical formulations, such as mixed integer linear programming (MILP). The major drawback of such approaches is great difficulty in capturing constraints and adjusting the model for changes. Additionally, MILP solvers are not guaranteed to yield a feasible solution in a timely manner. As a result, we chose to create an agent-based model (ABM). To the best of our knowledge, the presented problem has not been modeled before using ABM. Additionally, we sought to use a custom framework incorporating Behavior Trees (BTs) and Hierarchical Finite State Machines (HFSMs), two commonly used tools in the video game and robotics industry. We verify the model's correctness through numerical simulations and show that it is highly modular and extensible. [ABSTRACT FROM AUTHOR]

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information. [ABSTRACT FROM AUTHOR]

This paper discusses the development of position control for a rigid body payload carried by a team of quadrotors in the presence of external disturbance and uncertainty in model parameters. The formation of quadrotors is controlled and maintained during payload transportation for uniform load distribution and collision avoidance. In the controller structure, an adaptive sliding mode hierarchical control with the constant plus proportional rate reaching law is designed to compensate for the uncertainties that have unknown bounds. The finite-time stability of the closed-loop system is performed using Lyapunov stability analysis and the multiple time scale principle. Finally, the simulation results are shown to confirm the performance of the proposed controller in payload trajectory tracking without knowing its mass while the formation of the quadrotors is maintained. [ABSTRACT FROM AUTHOR]

In typical monitoring applications, such as fires or deforestation, the agents of the unmanned aircraft squadron must follow a circular motion. However, for other applications, including take off and landing, the squadron must obey the longitudinal formation. In this work an algorithm is proposed to reconfigure the unmanned aircraft squadron between its latitudinal and circular formations. The algorithm is designed by using a new approach based on segments. The time complexity of the proposed algorithm is analyzed and its correction is proved. The proof of correction is confirmed by the simulations. [ABSTRACT FROM AUTHOR]

A verifiable multiple UAV system cooperatively monitoring a road network is presented in this paper. The focus is on formal modelling and verification which can guarantee correctness of concurrent reactive systems such as multi-UAV systems. Kripke modelling is used to formally model the distributed cooperative control strategy, and to verify correctness of the specifications. Desirable properties of the mission such as liveness are specified in Computation Tree Logic (CTL). Model checking technique is used to exhaustively explore the state space to verify whether the system behaviour, modelled by Kripke model, satisfies the specifications. Violation of a specification is analysed by means of the counter-example generated by SMV model checking tool. [ABSTRACT FROM AUTHOR]

Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs. [ABSTRACT FROM AUTHOR]

Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs. [ABSTRACT FROM AUTHOR]

The foraging behaviour of bacteria in colonies exhibits motility patterns that are simple and reasoned by stimuli. Notwithstanding its simplicity, bacteria behaviour demonstrates a level of intelligence that can feasibly inspire the creation of solutions to address numerous optimisation problems. One such challenge is the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) to perform cooperative tasks for future autonomous systems. In light of this, this paper proposes a bacteria-inspired heuristic for the efficient distribution of tasks amongst deployed UAVs. The usage of multi-UAVs is a promising concept to combat the spread of the red palm weevil (RPW) in palm plantations. For that purpose, the proposed bacteria-inspired heuristic was utilised to resolve the multi-UAV task allocation problem when combating RPW infestation. The performance of the proposed algorithm was benchmarked in simulated detect-and-treat missions against three long-standing multi-UAV task allocation strategies, namely opportunistic task allocation, auction-based scheme, and the max-sum algorithm, and a recently introduced locust-inspired algorithm for the allocation of multi-UAVs. The experimental results demonstrated the superior performance of the proposed algorithm, as it substantially improved the net throughput and maintained a steady runtime performance under different scales of fleet sizes and number of infestations, thereby expressing the high flexibility, scalability, and sustainability of the proposed bacteria-inspired approach. • A new algorithm for autonomous task allocation in multi-UAVs missions is proposed. • A well-controlled experimental framework for evaluating the proposed algorithm is developed. • The proposed algorithm is investigated against four benchmark algorithms. • The results show significant improvements in considered measures. [ABSTRACT FROM AUTHOR]