Resilience-Aware Cooperative Mission Planning Algorithm for Multi-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 Multi-UAV Systems in Complex Dynamic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260138

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ZHAO xuejian, XIE lulu, WANG enliang. Resilience-Aware Cooperative Mission Planning Algorithm for Multi-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 Multi-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, Jiangsu 210003, China
2.
School of Modern Postal and Smart Transportation, Jiangsu 210003, China
3.
Postal Industry Technology R & D Center of the State Post Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Jiangsu 210003, China

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 mission planning for multiple UAV systems under complex dynamic environments, where dynamic task arrivals, UAV failures, no fly zone constraints, and link quality degradation coexist. Methods A resilience aware hybrid swarm optimization algorithm, termed RAHSO, is proposed. The method first builds an integrated task and route planning model that incorporates task value, route cost, energy consumption, interference penalty, time window constraints, capability constraints, conflict resolution, and link quality. It then combines clustering and genetic initialization, DBO and PSO hybrid search, GA and VNS local refinement, and Tarjan based deadlock detection and repair to obtain high quality baseline solutions. Furthermore, an event driven PPO online replanning module is introduced to rapidly adjust affected task subsets when emergent tasks, UAV failures, or topology changes occur. Results and Discussions Comparative and ablation experiments are conducted under static, scale-expansion, dynamic-event, and interruption scenarios. The results demonstrate that the proposed method achieves consistent advantages in task completion rate, total utility, average energy consumption, recovery time, and resilience index over representative baselines while maintaining acceptable replanning latency. Conclusions The proposed method provides an effective solution for resilient cooperative mission and route planning of multiple UAV systems in complex dynamic environments.
- multiple UAV systems,
- cooperative mission planning,
- task allocation,
- route planning,
- hybrid swarm intelligence optimization,
- resilient replanning

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References(28)

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