Resilience-Aware Cooperative Mission Planning Algorithm for Multiple UAV Systems in Complex Dynamic Environments

ZHAO Xuejian; XIE Lulu; WANG Enliang

doi:10.11999/JEIT260138

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ZHAO Xuejian, XIE Lulu, WANG Enliang. Resilience-Aware Cooperative Mission Planning Algorithm for Multiple UAV Systems in Complex Dynamic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260138

Citation:

ZHAO Xuejian, XIE Lulu, WANG Enliang. Resilience-Aware Cooperative Mission Planning Algorithm for Multiple UAV Systems in Complex Dynamic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260138

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Resilience-Aware Cooperative Mission Planning Algorithm for Multiple UAV Systems in Complex Dynamic Environments

doi: 10.11999/JEIT260138 cstr: 32379.14.JEIT260138

ZHAO Xuejian^{1, 2, 3
,
,},
XIE Lulu^{1, 2, 3},
WANG Enliang^{1, 3}

1.
Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Modern Postal and Smart Transportation, Nanjing 210003, China
3.
Postal Industry Technology R & D Center of the State Post Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received Date: 2026-02-02
Accepted Date: 2026-07-06
Rev Recd Date: 2026-07-06

Available Online: 2026-07-19

Abstract

Abstract

Objective This paper addresses the strongly coupled problem of task allocation and route planning in cooperative task and route planning for multiple UAV systems operating in complex dynamic environments, where dynamic task arrivals, UAV failures, no-fly-zone constraints, and link quality degradation occur simultaneously. Methods A Resilience-Aware Hybrid Swarm Optimization (RAHSO) algorithm is proposed. First, an integrated task-route planning model is established by jointly considering task value, route cost, energy consumption, interference penalties, time-window constraints, platform capability constraints, conflict resolution, and link quality within a unified optimization framework. High-quality initial solutions are generated through clustering-based and genetic initialization. A hybrid optimization framework that integrates the Dung Beetle Optimizer (DBO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Variable Neighborhood Search (VNS) is then employed to perform global exploration and local refinement. In addition, Tarjan-based deadlock detection and repair are incorporated to guarantee feasible task assignments. Finally, an event-driven Proximal Policy Optimization (PPO) online replanning module is designed to rapidly update affected task subsets in response to emergent tasks, UAV failures, and network topology changes. Results and Discussions Comparative and ablation experiments are conducted under static, large-scale, dynamic-event, and interruption scenarios. The results demonstrate that the proposed method consistently outperforms representative baseline algorithms in task completion rate, accumulated task value, average energy consumption, recovery time, and resilience index while maintaining satisfactory online replanning latency. Conclusions The proposed method provides an effective solution for resilient cooperative task and route planning for multiple UAV systems operating in complex dynamic environments.
- Multiple UAV systems,
- Cooperative task and route planning,
- Task allocation,
- Route planning,
- Hybrid Swarm Optimization,
- Resilient replanning

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References(28)

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