Cooperative Unmanned Aerial Vehicles Research Articles

Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review

This review paper provides insights into optimization strategies for Unmanned Aerial Vehicles (UAVs) in a variety of surveillance tasks and scenarios. From basic path planning to complex mission execution, we comprehensively evaluate the multifaceted role of UAVs in critical areas such as infrastructure inspection, security surveillance, environmental monitoring, archaeological research, mining applications, etc. The paper analyzes in detail the effectiveness of UAVs in specific tasks, including power line and bridge inspections, search and rescue operations, police activities, and environmental monitoring. The focus is on the integration of advanced navigation algorithms and artificial intelligence technologies with UAV surveillance and the challenges of operating in complex environments. Looking ahead, this paper predicts trends in cooperative UAV surveillance networks and explores the potential of UAVs in more challenging scenarios. This review not only provides researchers with a comprehensive analysis of the current state of the art, but also highlights future research directions, aiming to engage and inspire readers to further explore the potential of UAVs in surveillance missions.

Integrated the Artificial Potential Field with the Leader–Follower Approach for Unmanned Aerial Vehicles Cooperative Obstacle Avoidance

For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader’s attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient “few controlling many” paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms.

UV communication cooperative formation UAV alliance capture algorithm.

The capture of target unmanned aerial vehicles (UAVs) by a UAV formation is one of the important and typical tasks in multi-UAV battlefield operations. In this paper, an ultraviolet (UV) light communication-assisted formation UAV alliance capture algorithm is proposed, which combines UV light communication technology with a capture algorithm. With the help of wireless UV light to assist UAV formation inter-UAV data confidentiality transmission and non-line-of-sight communication, the algorithm integrates the alliance generation algorithm with the region minimization strategy, solves the optimal alliance structure by using the dynamic programming method, and implements the aerial capture of the target UAVs by using the region minimization strategy, so as to complete the task of efficiently capturing multi-targets by the UAV formation in complex scenarios. Simulation comparisons were conducted between the region minimization strategy and the proposed UV communication-assisted formation UAV alliance capture algorithm. The results show that the proposed algorithm reduces energy consumption by 12.73% on average and decreases the average number of algorithm iterations by 27.49% during the UAV formation capture of multiple targets, which verifies its low energy consumption and high capture efficiency.

Cooperative Unmanned Aerial and Surface Vehicles for Extended Coverage in Maritime Environments

Cooperative Unmanned Aerial and Surface Vehicles for Extended Coverage in Maritime Environments

Adaptive Fuzzy Quantized Control for a Cooperative USV-UAV System Based on Asynchronous Separate Guidance

This paper focuses predominantly on the multi-tasks carried out by the cooperative unmanned surface vehicle-unmanned aerial vehicle (USV-UAV) system in which the input quantization is considered. The proposed cooperative scheme consists of the asynchronous separate guidance and adaptive fuzzy quantized control algorithm. The proposed guidance law takes full advantage of subsystems whilst considering the maneuverability of these subsystems in order to achieve the goal of executing multi-tasks. In contrast to previous guidance laws, although the same waypoint path is planned, the calculation for guidance law proposed is based on speed rather than time, which in reality is more relevant. As for the controls, an adaptive fuzzy quantized controller was developed to reduce undue exertion on the actuator. By fusing the dynamic surface control (DSC) and fuzzy logic system (FLS), a hysteresis quantizer has been introduced to reduce the transmission load. By properly adjusting the quantization density, the number of quantizations was reduced whilst maintaining a favorable control performance. All of the stated variables are semi-global uniform ultimate bounded (SGUUB) and the stability of the USV-UAV system is proofed through the Lyapunov theorem. Finally, the advantages of the proposed scheme are evaluated by two simulative experiments, exhibiting the favorable tracking accuracy and reduced wear on the actuators.

UAV Swarm Mission Planning and Load Sensitivity Analysis Based on Clustering and Optimization Algorithms

Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.

Dynamic redeployment of UAV base stations in large-scale and unreliable environments

The deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BSs) has emerged as a promising solution to enhance communication services provided to ground users. However, deploying UAV-BSs faces challenges including the cooperation of multiple UAVs, dynamic user distribution, low-reliability issues of UAVs, and efficient redeployment in large environments. Existing literature addresses some of these challenges but lacks a comprehensive approach. In this paper, we investigate all these challenges and propose a novel deployment framework with the objective of maximizing the quality of communication service by dynamically deploying UAV-BSs. The proposed framework employs a decentralized approach, allowing UAV-BSs to locally adjust their locations and rapidly respond to changes, thus ensuring stable and efficient service supply to ground users. In addition, we implement multiple simulation experiments to evaluate the performance of the proposed framework in solving the UAV-BSs redeployment problem in large and dynamic environments. The results demonstrate its ability to effectively improve the quality of communication services.

Research on Scheme Design and Decision of Multiple Unmanned Aerial Vehicle Cooperation Anti-Submarine Based on Knowledge-Driven Soft Actor-Critic

To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward.

UAV-Assisted Maritime Legitimate Surveillance: Joint Trajectory Design and Power Allocation

This paper investigates a novel maritime wireless surveillance scenario, where a legitimate monitor vessel moves around to eavesdrop the suspicious communication from a suspicious unmanned aerial vehicle (UAV) to a suspicious vessel with the help of a cooperative UAV. Specifically, the cooperative UAV can adjust its jamming power and trajectory to exactly control the transmission rate of the suspicious link, thus improving the monitor vessel's surveillance performance. Furthermore, since the cooperative UAV cannot land or replenish energy on the sea surface, its jamming power allocation on the ocean should be carefully designed by the energy thresholds. Under such setup, we formulate a sum eavesdropping rate maximization problem, which jointly optimizes the jamming power and three-dimensional (3D) trajectory of the cooperative UAV, as well as the two-dimensional (2D) trajectory of the monitor vessel. To address this non-convex optimization problem, we decompose the design problem into three subproblems and propose an iterative algorithm to find its suboptimal solution. Numerical results show that the proposed jamming-assisted 3D joint design can significantly improve the eavesdropping rate and save the jamming power compared to the benchmark schemes.

Three-Dimensional Multi-UAV Trajectory Design for Cooperative Video Inspection and Uploading

This paper studies the multiple unmanned aerial vehicles (UAVs) trajectory design for post-disaster cooperative video inspection and information uploading. The locations to be inspected are modeled as points of interest (PoIs) with heterogeneous resolution requirements. The three-dimensional (3D) trajectories for multiple cooperative UAVs are designed to minimize the time for successfully inspecting all the PoIs and maximize the communication time for the video uploading. An UAV inspection model is built to clarify the criteria for successful inspection, which is related to the UAV's 3D positions and velocity. The proposed UAV trajectory design involves two steps. First, the PoIs in proximity are grouped using convex optimization. Then, the multiple UAV trajectories are jointly designed by solving a multiple traveling salesman problem (MTSP). Numerical results show that the proposed design can significantly reduce the mission completion time.

Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game

This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.

UAV-Assisted Secure Communication for Coordinated Satellite-Terrestrial Networks

This letter investigates an unmanned aerial vehicle (UAV)-assisted satellite-terrestrial communication network. Specifically, a three-dimensional mobile UAV helps to enhance coverage and simultaneously enable secure communication between the satellite and ground users with the internal eavesdropper. Moreover, non-orthogonal multiple access protocol is adopted, such that the spectral efficiency can be further improved. Aiming to maximize the secrecy rate, a joint cooperative jamming, transmit power, and UAV trajectory optimization problem is proposed. To deal with its non-convexity, we propose an iterative optimization algorithm that first optimally addresses the jamming and transmit power optimization in close forms and then uses an iterative relaxation technique to design the UAV trajectory for the given power. Simulation results highlight the critical role of the mobile UAV and significantly confirm the effectiveness of the proposed algorithm over the benchmark schemes.

Multi-UAV Cooperative Air Combat Decision-Making Based on Multi-Agent Double-Soft Actor-Critic

Multiple unmanned aerial vehicle (multi-UAV) cooperative air combat, which is an important form of future air combat, has high requirements for the autonomy and cooperation of unmanned aerial vehicles. Therefore, it is of great significance to study the decision-making method of multi-UAV cooperative air combat since the conventional methods are challenging to solve the high complexity and highly dynamic cooperative air combat problems. This paper proposes a multi-agent double-soft actor-critic (MADSAC) algorithm for solving the cooperative decision-making problem of multi-UAV. The MADSAC achieves multi-UAV cooperative air combat by treating the problem as a fully cooperative game using a decentralized partially observable Markov decision process and a centrally trained distributed execution framework. The use of maximum entropy theory in the update process makes the method more exploratory. Meanwhile, MADSAC uses double-centralized critics, target networks, and delayed policy updates to solve the overestimation and error accumulation problems effectively. In addition, the double-centralized critics based on the attention mechanism improve the scalability and learning efficiency of MADSAC. Finally, multi-UAV cooperative air combat experiments validate the effectiveness of MADSAC.

UAV-Assisted Online Machine Learning Over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning ( HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions. In doing so, we consider both micro (i.e., UAV-level) and macro (i.e., swarm-level) system design. At the micro-level, we propose network-aware HN-PFL, where we distributively orchestrate UAVs inside swarms to optimize energy consumption and ML model performance with performance guarantees. At the macro-level, we focus on swarm trajectory and learning duration design, which we formulate as a sequential decision making problem tackled via deep reinforcement learning. Our simulations demonstrate the improvements achieved by our methodology in terms of ML performance, network resource savings, and swarm trajectory efficiency.

Edge Computing Enabled Energy-Efficient Multi-UAV Cooperative Target Search

Multiple unmanned aerial vehicles (UAVs) cooperative search have been widely adopted for surveillance and search-related applications. For a certain search area, UAVs may need to search it repeatedly to obtain a high-confidence result about the target distribution in the search area. However, the short battery life and moderate computational capability restrict UAVs to repeatedly execute the computation-intensive and energy-consuming search tasks. To address the issue, in this paper, we utilize edge computing to develop a continual and cooperative UAV search mechanism. Specifically, we first establish an edge computing enabled multi-UAV cooperative search framework, in which the mobility model of UAV, search task computing and offloading models are presented. An uncertainty minimization problem is then formulated, aiming to obtain a high-efficiency and high-confidence search result at the unpredictable uncertainty in search area. Considering that round-trip energy consumption, offloading decision-making, and trajectory planning may contribute to the reduction in uncertainty, we propose an uncertainty minimization-based cooperative target search (UMCTS) strategy. Finally, extensive simulation results validate that UMCTS can outperform the existing strategies and achieve at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$89\%$</tex-math></inline-formula> performance gain on average uncertainty. Based on the results, we also present a comprehensive analysis and discussion on how different parameters affect the search performance.

Simulation of motions of a 6DoF unmanned aerial vehicle from the mathematical model in free space

This work prepares a smooth playing ground to modeling and simulation of a Collision Avoidance for Cooperative UAVs with Rolling Optimization Algorithm (ROA) Based on Predictive State Space (PSS) technique using Matlab, Simulink and Aerospace toolboxes of MathsWork. The methodological approach adopted is Computer Aided Software Engineering and to have handle collisions and maneuvers in a mathematical way, normal simulation of the trajectory of both the UAV and obstacles is defined by geometric approach. The goal of the work is to develop a mathematical model of a six degree of freedom (6DoF) of unmanned aerial vehicle (UAV) in free space for further modeling and simulation of a collision avoidance for cooperative UAVs with the required specifications. The motion (modeling of mathematical parameters like velocity, position, body rotational rate and Euler angles) of the 6DoF aircraft was determined by coordinate systems which allow the aircraft’s position and orientation in spaceto be kept tracked. The discrete form of the developed model for simulation was achieved using appropriate transfer functions. The development of the mathematical model will enable the development of an artificial NN model predictive controller (MPC) that can handle the nonlinearities associated with the UAV based on PSS technique.

Robust collaborative 3D target localization using UAVs with state uncertainties

This paper addresses the problem of target localization using measurements collected by a fleet of cooperative Unmanned Aerial Vehicles (UAVs). The positions and orientations of UAVs are partly unknown. The measurement noise and other uncertainties of the system are assumed to be unknown but bounded. Moreover, some of the UAVs of the feet may produce erroneous data (outliers). Several UAVs are assumed to observe a single target, to exchange the collected information. Then they have to characterize the set of all target locations consistent with the measurements, the bounded measurement noise, and the bounded uncertainty of the position and orientation of the UAVs while being robust to possible outliers. A set-membership approach based on set inversion via interval analysis is used. Thick intervals are required to account for the uncertainty in the location and orientation of the UAVs. The robustness to outliers is obtained using q-relaxed intersection.

A Survey on the Design Aspects and Opportunities in Age-Aware UAV-Aided Data Collection for Sensor Networks and Internet of Things Applications

Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified.

Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks

Missions involving Unmanned Aerial Vehicle usually consist of reaching a set of regions, performing some actions in each region, and returning to a determined depot after all the regions have been successfully visited or before the fuel/battery is totally consumed. Hence, planning a route becomes an important task for many applications, especially if a team of Unmanned Aerial Vehicles is considered. From this team, coordination and cooperation are expected to optimize results of the mission. In this paper, a system for managing multiple cooperative Unmanned Aerial Vehicles is presented. This system divides the routing problem into two stages: initial planning and routing solving. Initial planning is a first step where the regions to be visited are grouped in multiple clusters according to a distance criterion, with each cluster being assigned to each of the Unmanned Aerial Vehicles. Routing solving computes the best route for every agent considering the clusters of the initial planning and a variant of the Orienteering Problem. This variant introduces the concept of shared regions, allowing an Unmanned Aerial Vehicle to visit regions from other clusters and compensating for the suboptimal region clustering of the previous stage. The Orienteering Problem with shared regions is solved using the deep learning architecture Transformer along with a deep reinforcement learning framework. This architecture is able to obtain high-quality solutions much faster than conventional optimization approaches. Extensive results and comparison with other Combinatorial Optimization algorithms, including cooperative and non-cooperative scenarios, have been performed to show the benefits of the proposed solution.