关键词: 改进的双向快速搜索树, 椭球化采样?, 双目标偏差策略, 自适应交替探索

Abstract: Aiming at the path planning requirements of unmanned aerial vehicles (UAVs) in complex three-dimensional building environments under the background of the low-altitude economy, an improved bidirectional rapidly-exploring random tree adaptive alternating dual-target deviation search algorithm (ST-BA-RRT*) is proposed. During the sampling stage of this algorithm, the two-dimensional elliptical sampling is improved to ellipsoidal sampling in a three-dimensional environment, and in conjunction with the dual-target deviation strategy, it suppresses the expansion of the random tree into the obstacle area and guides it to grow towards the target directionally. In the expansion stage, the adaptive alternating exploration and improved artificial potential field auxiliary strategies are applied, which enhances the algorithm's environmental adaptability and local obstacle avoidance ability. In the collision detection link, a new cost function is designed to reduce the frequency of obstacle inspections and optimize the planning time. The connectivity processing utilizes bidirectional random sampling to improve the planning efficiency. Finally, the -spline function is used to smooth the path. The experimental results show that compared with existing algorithms, the path generated by the ST-BA-RRT* algorithm is shorter and smoother, and the average path planning time is reduced by 35%. It has significant advantages in terms of path quality and environmental adaptability, can efficiently generate optimized flight trajectories, and meet the path planning requirements of UAVs in complex three-dimensional building environments.

Key words: improved bidirectional rapidly-exploring random tree, ellipsoidal sampling, dual-target bias strategy, adaptive alternating exploration