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.
Abstract
In view of the task allocation problem under communication constraints, this paper proposes a distributed performance impact (DPI) algorithm for heterogeneous multi-UAV cooperation. First, the task allocation problem is modelled, incorporating task requirements, heterogeneous flight performance and non-ideal networked communication. Then, a multi-phase DPI algorithm is designed in five stages: task inclusion, cluster generation, conflict resolution, coalition formation and task segmentation. Based on task inclusion, a communication-reachable networked cluster generation method is developed. The conflict resolution mechanism is improved based on the removal-performance-impact and task contribution rate. Further considering independently unachievable tasks, 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. Finally, simulations are conducted to demonstrate the effectiveness, superiority and robustness of the proposed DPI algorithm.
This paper proposes a collaborative task allocation model for heterogeneous multi-UAV systems in missions with chain-structured task priorities and strict time constraints. The model considers key constraints, including UAV flight speed, task capability differences, execution limits, and ordered task dependencies, enabling accurate representation of coordinated chain task execution. Traditional approaches struggle to ensure both real time performance and feasibility when the number of tasks and UAVs grows. To address this challenge, we develop an Extended Consensus-Based Bundle Algorithm (E-CBBA). The algorithm extends the traditional CBBA framework by introducing a maximum gain task screening strategy, a feasible position set for path insertion, and a global task execution time matrix for distributed consensus and conflict resolution. These mechanisms ensure that each UAV evaluates and inserts tasks only under feasible priority, timing, and kinematic conditions. We prove the convergence and approximate optimality of E-CBBA. Extensive simulations show that E-CBBA achieves higher objective values and task completion rates than traditional CBBA and GA-based methods, while maintaining fast convergence, scalability to multiple sequential task types, robustness under moderate communication degradation, and feasibility in ROS 2 and Gazebo simulations. Note to Practitioners—Multiple unmanned aerial vehicles (UAVs) are increasingly deployed in real combat and emergency response missions, where tasks are often subject to strict time constraints and precedence relationships. As the number of tasks and UAVs grows, existing task allocation methods tend to suffer from excessive decision latency and frequent task conflicts, which severely degrade coordination efficiency. To address these challenges, we propose an extended consensus-based bundle algorithm (E-CBBA) that enables UAVs to rapidly identify feasible tasks and achieve coordinated task allocations while explicitly respecting task ordering and temporal constraints. The proposed algorithm extends the conventional CBBA framework by incorporating task dependency modeling and time feasibility checking, allowing scalable and conflict-free coordination in complex mission scenarios. Simulations demonstrate that the method significantly improves task completion rates and decision speed, and remains effective as the number of tasks and UAVs increases. Future work will incorporate online learning to enhance robustness. The algorithm can also be extended to multi-robot coordination scenarios such as emergency rescue, logistics delivery, and inspection missions.
Kang Wang, Dongzhao Wang, Meng-Zhen Li et al.· IEEE Transactions on Automat...· 0 citations
This paper analyzes the factors affecting communication interactions between UAVs and proposes a bidding-based grouping method to eliminate ineffective communication interactions, and introduces a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure.
Wei-xing Xia, Peng Chen, Feifei Song et al.· Drones· 0 citations
Simulation results demonstrate that, compared with P-DQN, SAC, and PADDPG, the proposed framework achieves superior joint performance, thereby verifying its effectiveness for multi-UAV ISAC joint optimization.
Guifen Chen, Zeli Gong· Digital Signal and Computer...· 0 citations
Multi-UAV mobile edge computing can provide flexible computing services for ground users, but it introduces coupled decisions in user association, channel allocation, cooperative offloading, and power control. This paper studies collaborative task offloading in a multi-UAV-assisted MEC system. A multi-stage service process is modeled, including user-to-UAV uploading, local computation, UAV-to-UAV cooperative transmission, cooperative computation, and result return. A comprehensive performance objective is formulated by jointly considering task success rate, delay cost, and energy consumption. To solve the resulting mixed discrete-continuous sequential decision problem, we propose GNN-MAHPPO, a multi-agent hybrid-action PPO algorithm enhanced by dual-layer heterogeneous graph attention. The proposed method jointly optimizes user association, channel allocation, cooperative offloading ratios, and transmit powers. Simulation results show that the proposed method achieves a higher task success rate and lower completion delay and energy consumption than the representative baselines, demonstrating its effectiveness in coordinating access, cooperation, and resource allocation.
Xin-Miao Zhu, Yupeng Wang· 2026 8th International Confe...· 0 citations
A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.
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.
Zongwei Li, Guang Zhang, Heyun Gao· Journal of Vibration and Con...· 0 citations