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<p>Multi-unmanned aerial vehicle (multi-UAV) target tracking requires coordinated trajectory planning that simultaneously ensures formation safety, target visibility, and motion smoothness. To address this challenge, we propose a distributed framework integrating heuristic-guided path search with spatiotemporal trajectory optimization. In the front end, each UAV constructs a safe area that accounts for both tracking distance and visibility considerations, and selects feasible expansion points for path search based on the desired tracking position. Subsequently, a unified trajectory optimizer is employed to derive an executable trajectory that complies with extensive constraints, predicated on the initial path configurations. Considering that UAVs may lose visibility of the target due to cluttered obstacles, a field-of-view sharing mechanism is proposed, enabling occluded UAVs to leverage teammates’ visual data for robust collaborative planning in cluttered environments. Finally, simulations and comparative results are conducted through a visualization platform to validate the superior performance of the proposed multi-UAV trajectory planning framework for target tracking.</p>

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Multi-UAV trajectory planning with field-of-view sharing mechanism in cluttered environments: application to target tracking

  • 期刊论文

  • 发表日期：

    2025年4月25日

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  • Kai Rao,
  • Huaicheng Yan,
  • Penghui Yang,
  • Meng Wang,
  • Yunkai Lv

摘要

Multi-unmanned aerial vehicle (multi-UAV) target tracking requires coordinated trajectory planning that simultaneously ensures formation safety, target visibility, and motion smoothness. To address this challenge, we propose a distributed framework integrating heuristic-guided path search with spatiotemporal trajectory optimization. In the front end, each UAV constructs a safe area that accounts for both tracking distance and visibility considerations, and selects feasible expansion points for path search based on the desired tracking position. Subsequently, a unified trajectory optimizer is employed to derive an executable trajectory that complies with extensive constraints, predicated on the initial path configurations. Considering that UAVs may lose visibility of the target due to cluttered obstacles, a field-of-view sharing mechanism is proposed, enabling occluded UAVs to leverage teammates’ visual data for robust collaborative planning in cluttered environments. Finally, simulations and comparative results are conducted through a visualization platform to validate the superior performance of the proposed multi-UAV trajectory planning framework for target tracking.