Robot path planning based on bidirectional rapidly exploring random tree algorithm

With the development of robot science and technology, robots are increasingly involved in human activities, from simple household use to intelligent unmanned factories, to various explorations in the sky, earth, and sea, all of which rely on the help of robots. In these scenarios, the path autonomously planned by robots may have issues such as insufficient accuracy and low efficiency. In order to solve the above problems, this paper studies and proposes a robot path planning method based on bidirectional fast expansion random tree algorithm. The method first constructs different obstacle environment maps, including 12 rectangular obstacles, 12 circular obstacles, and a mixture of rectangular and circular obstacles; Then, based on the principle of bidirectional fast expansion random tree algorithm, the process of path search was implemented; The final simulation results show that in both simple and complex obstacle environments, the algorithm outperforms traditional algorithms in terms of planning time, path length, success rate, number of path nodes, and path smoothness. This provides an effective solution for efficient path planning of robots in complex environments.