Multiple Unmanned Aerial Vehicles Research Articles

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.

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.

Distributed edge-based event-triggered optimal formation control for air-sea heterogeneous multiagent systems

This paper addresses the optimal formation control problem for a class of air-sea heterogeneous multiagent systems with external disturbances and model uncertainties. Multiple unmanned aerial vehicles (UAVs) and multiple unmanned surface vessels (USVs) are considered in this system. First, a distributed adaptive state compensator is designed for each agent to estimate the state information of the virtual leader. This compensator is equipped with an edge-based event-triggered scheme to reduce the amount of communication for each edge. Second, a reinforcement learning-based optimal formation controller with the appointed-time prescribed performance is designed to obtain the formation configuration for an air-sea system. This method not only optimizes the formation controller but also ensures the steady-state accuracy and stabilization time. Additionally, an event-triggered mechanism is proposed to reduce the number of optimal formation controller communications. A self-structuring actor-critic neural network and a self-structuring radial basis function neural network are also proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation and approximate the uncertainty, respectively. It can constantly adjust the number of neurons, eventually obtaining an optimal number. Finally, it is proven by Lyapunov theory that all signals are semiglobally uniform and eventually bounded, and the simulation results are provided to illustrate the effectiveness of the scheme.

Learning-Based Collaborative Computation Offloading in UAV-Assisted Multi-Access Edge Computing

Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of Things devices (IOTDs) remains a significant challenge for UAVs due to their limited computing and communication capabilities. Consequently, optimizing and managing the constrained computing, communication, and energy resources of UAVs are essential for establishing an efficient aerial network infrastructure. To address this challenge, we investigate the collaborative computation offloading optimization problem in a UAV-assisted MEC environment comprising multiple UAVs and multiple IODTs. Our primary objective is to obtain efficient offloading strategies within a multi-heterogeneous UAV environment characterized by limited computing and communication capabilities. In this context, we model the problem as a multi-agent markov decision process (MAMDP) to account for environmental dynamics. We employ a multi-agent deep deterministic policy gradient (MADDPG) approach for task offloading. Subsequently, we conduct simulations to evaluate the efficiency of our proposed offloading scheme. The results highlight significant improvements achieved by the proposed offloading strategy, including a notable increase in the system completion rate and a significant reduction in the average energy consumption of the system.

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.

Information Entropy-Based Cooperative Surveillance in Areas with Obstacles

This paper solves the problem of searching a surveillance region and tracking possible radio emitters by multiple unmanned aerial vehicles cooperating through communication links in a decentralized manner. An innovative entropy-based approach is utilized to obtain the routes and tasks (search/track) for the vehicles. Another significant contribution of this research is the assumption of each vehicle being equipped with only one simple and cost-effective direction finding (DF) sensor. The routes and tasks are determined by minimizing a well-defined entropy-based cost function. The proposed surveillance scheme allows one to find a feasible and near-optimal solution. The algorithm’s efficiency is verified through various simulations.

Fast Automatic Registration of UAV Images via Bidirectional Matching.

Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB-the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.

High-precision Time Synchronization Algorithm for Unmanned Aerial Vehicle Ad Hoc Networks based on Bidirectional Pseudo-range Measurements

The increasing demand for missions involving unmanned aerial vehicles (UAVs) raises the need for reliable and accurate time synchronization in ad hoc networks. Time synchronization plays a crucial role in coordinating the actions of multiple UAVs, particularly when it comes to positioning, coordination, and data fusion. However, achieving high-precision time synchronization is challenging due to the difficult-to-estimate communication transmission delay, measurement noise, relative motion, and clock source characteristics in wireless ad hoc networks formed by UAVs. To address this issue, this paper proposes a fully distributed high-precision network time synchronization algorithm that is suitable for multi-hop dynamic ad hoc networks. The proposed algorithm is based on bidirectional pseudo-range measurements, graph theory, and random matrix correlation theory. It is composed of relative clock skew estimation, logic clock skew compensation and logic clock offset compensation. The algorithm is rigorously derived, requiring no central node, and it is capable of obtaining relative clock skew even when nodes broadcast clock information asynchronously. In addition, the proposed algorithm enables real-time elimination of transmission delays and has noise-resistant capabilities. Simulation results validate the effectiveness of the algorithm, demonstrating that the time synchronization accuracy in UAV ad hoc networks can reach the sub-nanosecond level.

GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks

Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking traditional infrastructure. However, UAVs’ limited onboard energy storage necessitates optimized, energy-efficient communication strategies and intelligent energy expenditure to maximize productivity. This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations. The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure. By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints, the mixed integer linear programming approach reduces energy usage by 9.1 ​% versus conventional greedy heuristics. The key results provide insights into separating charging strategies based on UAV mobility patterns, fully utilizing all available infrastructure through balanced distribution, and strategically leveraging existing base stations before deploying dedicated charging assets. Compared to myopic localized decisions, the globally optimized solution extends battery life and enhances productivity. Overall, this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets. The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

A vision-only relative distance calculation method for multi-UAV systems

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).

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

A highly effective control method is very important to guarantee the safety of the formation of flying missions for multiple unmanned aerial vehicles (UAVs), especially in the presence of complex flying environments and actuator constraints. In this regard, this paper investigates the formation tracking control problem of multiple UAVs in the presence of actuator saturation. Firstly, a brand-novel finite-time anti-saturated control scheme is proposed for multiple UAVs to track the desired position commands, wherein the tracking performance is tuned by introducing a logarithmic function-based state-mapping policy. Then, an adaptive scheme based on projection rules is devised to compensate for the negative effects brought by the actuator saturation. Based on the proposed formation tracking controller, the finite-time formation tracking performance tuning and control saturation problems can be addressed simultaneously with a comparatively allowable system robustness. Finally, three groups of illustrative examples are organized to verify the effectiveness of the proposed formation tracking control scheme.

Retracted: Finite-Time Attitude Cooperative Control of Multiple Unmanned Aerial Vehicles via Fast Nonsingular Terminal Sliding Mode Control

Retracted: Finite-Time Attitude Cooperative Control of Multiple Unmanned Aerial Vehicles via Fast Nonsingular Terminal Sliding Mode Control

Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography.

Nowadays, wireless sensor networks (WSNs) have a significant and long-lasting impact on numerous fields that affect all facets of our lives, including governmental, civil, and military applications. WSNs contain sensor nodes linked together via wireless communication links that need to relay data instantly or subsequently. In this paper, we focus on unmanned aerial vehicle (UAV)-aided data collection in wireless sensor networks (WSNs), where multiple UAVs collect data from a group of sensors. The UAVs may face some static or moving obstacles (e.g., buildings, trees, static or moving vehicles) in their traveling path while collecting the data. In the proposed system, the UAV starts and ends the data collection tour at the base station, and, while collecting data, it captures images and videos using the UAV aerial camera. After processing the captured aerial images and videos, UAVs are trained using a YOLOv8-based model to detect obstacles in their traveling path. The detection results show that the proposed YOLOv8 model performs better than other baseline algorithms in different scenarios-the F1 score of YOLOv8 is 96% in 200 epochs.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

A novel multi-objective evolutionary algorithm with a two-fold constraint-handling mechanism for multiple UAV path planning

Most multiple unmanned aerial vehicle (UAV) path planning problems are often treated as constrained single-objective optimization problems. How to consider them as constrained multi-objective optimization problems (CMOPs) have seldom been explored in this field. To fill this gap, this paper firstly constructs multiple UAV path planning problem as a CMOP with two objectives and five constraints. Then a novel multi-objective evolutionary algorithm with a two-fold constraint-handling mechanism is proposed for multiple UAV path planning. To cope with constraints effectively, a constraint-handling technique based on a progressive weight vector strategy is proposed. Besides, a constraint repair technique that considers the flying environment is designed to further guide the algorithm to find feasible promising regions. Eight multiple UAV path planning test instances with different solving difficulties are constructed. Subsequently, they are used to validate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is superior over four compared algorithms in terms of obtaining a set of better-distributed Pareto optimal solutions.

Simultaneous search and monitoring by multiple aerial robots

This paper studies simultaneous search and monitoring (SSM) between multiple unmanned aerial vehicles (UAVs) and multiple moving ground targets. Searching for unknown targets and monitoring known ones are two intrinsically related problems, but they have mostly been addressed in isolation. We combine the two tasks and exploit their interconnection as a synergy rather than a trade-off. We construct the single-robot SSM as a partially observable Markov decision process (POMDP) and the multi-robot SSM as a semi-decentralised POMDP (semi-Dec-POMDP). A novel heuristic reactive policy planning is proposed to solve the POMDP. It is then extended for semi-Dec-POMDP with game-theoretical methods. In simulations and experiments, the searchers will successfully locate unknown targets without losing known ones and cooperate by partitioning their tasks. With theoretical proofs, simulations, and experiments, we demonstrate that our method can perform better than conventional approaches and the state-of-the-art.

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.

Channel Assignment and Power Allocation Utilizing NOMA in Long-Distance UAV Wireless Communication

Non-orthogonal multiple access (NOMA) is indeed an important technique of 5 G and beyond 5 G (B5G) communication systems, as it can increase the spectrum efficiency and system capacity under the same bandwidth. Long-distance unmanned aerial vehicle (UAV) networks, comprising a base station and multiple UAVs, are commonly used for reconnaissance purposes due to the extended flight range of the UAVs, which can provide broad coverage and satisfy the requirements for real-time communication. To alleviate spectrum underutilization of single channel orthogonal multiple access, we investigate resource allocation in UAV networks by utilizing NOMA and then formulate an optimization problem that maximizes the network capacity. The formulated problem can be decomposed into two sub-problems, namely UAV grouping and power allocation optimization. Accordingly, we present a UAV grouping method with low complexity according to the difference of channel gain between UAVs and base station, in which the UAVs with larger difference will be in the same cluster such that they can transmit signals over the same channel. Then, a power allocation optimization algorithm based on Lagrange multiplier method is then used to optimize the transmit power of UAVs utilizing the same channel. This algorithm considers the quality of service requirements for each UAV, and assigns power levels that maximize the overall network capacity. Simulation results have shown that the presented algorithms improve network capacity by approximately 19% compared with that of orthogonal multiple access, which demonstrates the effectiveness of utilizing NOMA in long-distance UAV networks.

Joint Optimization of 3D Placement and Radio Resource Allocation for Per-UAV Sum Rate Maximization

Unmanned aerial vehicles (UAV) have emerged as a practical solution that provides on-demand services to users in areas where the terrestrial network is non-existent or temporarily unavailable, e.g., due to natural disasters or network congestion. In general, UAVs' user-serving capacity is typically constrained by their limited battery life and the finite communication resources that highly impact their performance. This work considers the orthogonal frequency division multiple access (OFDMA) enabled multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive an efficient technique for the allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: (i) 3D placement and user association and (ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after fewer than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, results showed that the optimal power and sub-carrier allocation help to mitigate the inter-cell interference that directly impacts the system's performance.

Multi-UAV task allocation based on GCN-inspired binary stochastic L-BFGS

Task allocation has been one of the key issues for cooperative control of multiple unmanned aerial vehicles (Multi-UAVs), which has attracted a large number of researchers to conduct research in recent years. As the number of tasks and resource types increase, the solution time of most of the existing methods increases sharply, and are difficult to be deployed in other scenarios. To deal with task allocation problems with large-scale tasks and multiple types of resources, this paper proposed a multi-UAV task allocation method based on graph convolutional network (GCN)-inspired binary stochastic L-BFGS (GBSL-BFGS) with strong generalization. First, the objectives and constraints of the task allocation problem are analyzed, while a flexible and easily scalable method for describing the task allocation problem is proposed. Then, the GBSL-BFGS task allocation method is proposed for large-scale multi-UAV cluster. By introducing GCN as a graph mapper, the L-BFGS algorithm is able to optimize the binary decision matrix in the task allocation problem. Simulation experiments demonstrated that the GBSL-BFGS optimization method has a better performance and computational efficiency compared with other methods, especially for large-scale multi-UAV task allocation problems.