Low-Altitude Path Planning Based on Improved RRT* Algorithm
Qiang Yu, Yuchen Yao, Fengtao Zhang, Pengcheng Hao · 2024
To address the issues of low sampling efficiency, numerous redundant nodes, and high randomness in planning paths with the RRT algorithm, especially in low-altitude complex environments, an enhanced path planning method based on the improved RRT* algorithm is proposed. The improved algorithm introduces the target bias strategy and partition sampling strategy in the sampling process to optimize the original sampling strategy, which reduces the randomness of sampling; introduces the artificial potential field and gravity potential field to optimize the node tree node expansion strategy, which makes the generated algorithm more in line with the trajectory requirements under the low-altitude mission; at the same time, adopts the pruning strategy and the B-spline curve to prune the redundant path points, ensuring the final path aligns better with the flight vehicle's dynamic constraints. The simulation results show that the algorithm can obtain smooth paths to meet the requirements of low-altitude missions with higher efficiency in a three-dimensional environment.