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This paper proposes a rapid path planning algorithm based on the Rapidly-Exploring Random Tree (RRT), enhanced with Beetle Antennae Search (BAS) to improve unmanned aerial vehicle (UAV) operational efficiency in complex urban environments. The algorithm combines the random expansion capability of RRT with the directed exploration feature of BAS to quickly generate an optimized path in three-dimensional (3D) environments. The objective is to create a feasible, collision-free path from a designated start point to a target location, considering terrain complexity. Simulation results show that, in terms of both path quality and convergence speed, the RRT-BAS algorithm significantly outperforms RRT, BAS, RRT*, and RRT*-BAS.

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A Rapid Path Planning Method Based on RRT-BAS for Unmanned Aerial Vehicles in Urban Environments

  • 会议论文

  • 发表日期：

    2025年

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  • Yuanjun Zhu,
  • Bingjie Yang,
  • Xuejun Zhang,
  • Weidong Zhang

摘要

This paper proposes a rapid path planning algorithm based on the Rapidly-Exploring Random Tree (RRT), enhanced with Beetle Antennae Search (BAS) to improve unmanned aerial vehicle (UAV) operational efficiency in complex urban environments. The algorithm combines the random expansion capability of RRT with the directed exploration feature of BAS to quickly generate an optimized path in three-dimensional (3D) environments. The objective is to create a feasible, collision-free path from a designated start point to a target location, considering terrain complexity. Simulation results show that, in terms of both path quality and convergence speed, the RRT-BAS algorithm significantly outperforms RRT, BAS, RRT*, and RRT*-BAS.