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<p>The cooperative fencing control of multi-target by unmanned aerial vehicle (UAV) swarm in dynamic environment faces challenges, including unknown target states, abrupt motion mode switching, and node failures. This paper proposes a self-organizing cooperative fencing control strategy with adaptive task scheduling capability. First, a distributed prescribed-time target state observer (DPTTSO) is designed, enabling the swarm to estimate the targets’ unknown velocity and acceleration even when only a subset of UAVs can access the targets’ position. Second, a distributed controller integrating navigation, coordination, collision avoidance is developed, allowing the swarm to autonomously form and maintain stable fencing configurations without relying on predefined formations. Third, a dynamic task scheduling method combining a group-triggering mechanism and a multi-round auction strategy is proposed to handle emergencies, facilitating autonomous mode switching and resource reallocation. Simulation results demonstrate the effectiveness and robustness of the proposed method in terms of state estimation, configuration formation, collision and obstacle avoidance, and dynamic reorganization in multi-target dynamic scenarios.</p>

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Adaptive cooperative fencing and dynamic reorganization control of UAV swarm for multiple maneuvering targets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Hao Yu,
  • Xiu-xia Yang,
  • Yi Zhang

摘要

The cooperative fencing control of multi-target by unmanned aerial vehicle (UAV) swarm in dynamic environment faces challenges, including unknown target states, abrupt motion mode switching, and node failures. This paper proposes a self-organizing cooperative fencing control strategy with adaptive task scheduling capability. First, a distributed prescribed-time target state observer (DPTTSO) is designed, enabling the swarm to estimate the targets’ unknown velocity and acceleration even when only a subset of UAVs can access the targets’ position. Second, a distributed controller integrating navigation, coordination, collision avoidance is developed, allowing the swarm to autonomously form and maintain stable fencing configurations without relying on predefined formations. Third, a dynamic task scheduling method combining a group-triggering mechanism and a multi-round auction strategy is proposed to handle emergencies, facilitating autonomous mode switching and resource reallocation. Simulation results demonstrate the effectiveness and robustness of the proposed method in terms of state estimation, configuration formation, collision and obstacle avoidance, and dynamic reorganization in multi-target dynamic scenarios.