Multi-Objective Distributed Task Allocation for UAV Swarms with Limited Interactions

Dynamic Multiobjective Co-Optimization of Unmanned Aerial Vehicle Placement, Task Scheduling, and Resource Allocation for Remote Sensing

This article investigates the dynamic multiobjective co-optimization problem in unmanned aerial vehicle (UAV)-assisted remote sensing systems, aiming to jointly optimize UAV placement, task scheduling strategies, and computing/communication resource allocation to minimize the system’s average processing latency and the total energy consumption of UAVs. Addressing the shortcomings of existing research, which often overlooks the computational capabilities of UAVs, optimizes only a single aspect, and fails to account for environmental dynamics, this work formulates the problem as a dynamic multiobjective optimization problem. A hybrid optimization framework named DSG, integrating swarm intelligence and evolutionary algorithms, is proposed. The framework first derives a closed-form optimal resource allocation solution for given deployment and scheduling strategies through theoretical analysis. It then employs an improved dynamic multiobjective evolutionary algorithm (DMOEA) to co-optimize UAV positions (continuous variables) and task scheduling (discrete variables). Experimental results demonstrate that DSG achieves significantly better normalized hypervolume performance than comparative algorithms across various system scales [number of UAVs, access points (APs), and sensors] while exhibiting good stability and scalability. This provides an effective solution for the efficient co-optimization of UAV-assisted edge computing in dynamic environments.

Heterogeneous Networked Multi-UAV Cooperative Task Allocation Based on a Distributed Performance Impact Algorithm

A dual-fallback mechanism is constructed by a pruning-redundancy coalition formation scheme and a task- segmentation scheme, thereby enhancing both system operational efficiency and task-completion certainty of the proposed DPI algorithm.

Cooperative Multi-UAV Target Exploration with Graph-Based Reinforcement Learning

Unmanned aerial vehicles (UAVs) offer several advantages, including high mobility, flexible deployment, low cost, and strong adaptability to complex environments, making them highly promising for applications such as disaster search and rescue, environmental monitoring, inspection, and reconnaissance. For target exploration tasks in unknown environments, multiUAV systems can expand the search area, improve exploration efficiency, and enhance the robustness of task execution through cooperation, which makes this problem of significant research interest. However, such tasks still face several challenges, including partial observability of environmental information, complex cooperative decision-making, and difficulties in credit assignment among multiple UAVs. Reinforcement learning is capable of learning decision-making policies autonomously through interaction with the environment, providing a new perspective for solving cooperative exploration problems in complex environments. To address these issues, we propose a cooperative decision-making method for multi-UAV target exploration. By incorporating target-related information, the proposed method enhances the cooperative exploration capability of UAVs in unknown environments, while a tailored reward design is adopted to improve the coordination efficiency of multiple UAVs. Experimental results show that the proposed method exhibits strong adaptability to different team sizes and sensor configurations, learns effective cooperative behaviors, and outperforms classical exploration methods across multiple performance metrics, thereby demonstrating its effectiveness in multi-UAV target exploration tasks.

Centralized Scheduling and Distributed Trajectory Optimization for Cooperative Mapping with Multiple PX4-Based VTOL UAVs

Multi-UAVs cooperative task allocation and path planning for low-altitude logistics

To address the challenges of collaborative task allocation and path planning for multiple logistics unmanned aerial vehicles (UAVs) in urban low-altitude environments, this paper proposes a bilevel nested joint optimization method based on reinforcement learning and a graph search algorithm to enhance the efficiency of collaborative last-mile delivery by multiple logistics UAVs while reducing flight risks. The proposed method constructs a bilevel architecture system based on a task allocation and decision-making model and a path planning model. The upper-level model holistically considers the demands of three stakeholders—government (safety), customers (timeliness), and UAV enterprises (economy)—at the macro level. Then, based on real-time order information and UAV status, a multi-objective optimization and constraint model is constructed under complex dynamic environments. A multi-agent proximal policy optimization algorithm is employed to achieve rapid dynamic task allocation and decision-making. The lower-layer model utilizes the upper-level allocation results combined with detailed environmental information to plan safe and efficient flight paths for each UAV at the micro level. It employs an improved jumping-point search algorithm for refined path optimization. A loop feedback mechanism is designed to facilitate information exchange between layers, thereby coupling the task allocation and path planning processes to achieve collaborative optimization of upper- and lower-level task allocation and decision-making. This method effectively addresses complex logistics delivery scenarios, enhancing the overall efficiency and robustness of the delivery system. Simulation experiments comprehensively consider path influences from flexible open-area delivery, varying numbers of distribution centers and UAVs, and on-demand rush orders. Tests conducted in medium- and high-density environments demonstrate the proposed model and algorithm’s significant superiority in dynamic complex scenarios. Even when confronted with complex environments and dynamic order scenarios, it consistently generates highly applicable UAV flight paths.

Distributed Optimization-Based Joint Task Assignment and Bandwidth Allocation for UAV-Enabled Jammer Swarm

Owing to the cost-efficiency, low observability and swarm coordination capability, UAV-enabled swarm jamming is recognized as a significant countermeasure against networked radar systems. To fully exploit the collaborative jamming effectiveness of the swarm, it is critically important to optimize the task assignment among jammers. However, existing jamming task assignment algorithms still exhibit insufficient consideration of non-ideal factors in adversarial scenarios. 1) The frequency agility of radars is frequently overlooked, leading to the rare consideration of bandwidth allocation for jammers. 2) The vulnerability of jammers is often neglected, resulting in insufficient consideration being given to the robustness of task assignment algorithms. To address these issues, a distributed optimization-based joint task assignment and bandwidth allocation method is studied for the UAV-enabled jammer swarm in this paper. Firstly, we design the utility function of joint task assignment and bandwidth allocation, and formulate the optimization problem model. Subsequently, to facilitate the implementation of distributed optimization, a decomposition approach based on coalition formation games (CFG) and alternating direction method of multipliers (ADMM) is proposed for the formulated joint task assignment and bandwidth allocation problem. Finally, a distributed optimization-based joint task assignment and bandwidth allocation algorithm is proposed. Experimental results indicate that the proposed method demonstrates higher optimization efficiency and robustness compared to the centralized optimization.