An Improved Goal-bias RRT algorithm for Unmanned Aerial Vehicle Path Planning

Aiming at the problems of Rapidly-Exploring Random Tree (RRT) algorithm for Unmanned Aerial Vehicle (UAV) path planning, such as inefficient searching, no local planning, and unable to avoid dynamic obstacles effectively, an optimized Goal-bias RRT algorithm is proposed. Firstly, a probability factor is introduced to optimize the sampling method and reduce the global path planning time. Secondly, local path planning idea is introduced to avoid dynamic obstacle. Finally, a node selection strategy is used to re-planning local path so that the running speed and success rate are improved. Combined with the UAV body constraints, trajectory smoothing is carried out using cubic spline interpolation. The simulation experiment results show that the proposed method not only shortens the planning time in comparison with the Goal-bias RRT algorithm, but also has the ability to efficiently deal with dynamic path planning.