Research On Path Planning of Mobile Robot Based On Improved RRT* Algorithm

Abstract

Rapidly-Exploring Random Tree (RRT) has been widely used in robot navigation as a fast and efficient path planning method. Rapidly-Exploring Random Tree Star (RRT*) is one of the extensions of RRT, which outperforms the RRT algorithm because it can approximate the optimal path as the number of iterations increases. However, as the number of sampling points increases, the time to obtain the optimal path is too long, the algorithm converges significantly slower, and it is difficult to pass narrow obstacles in a short time. In this paper, we propose an improved RRT* algorithm, which first samples nodes in the dynamic target region to narrow the sampling range; expands tree nodes by target biasing strategy to reduce the randomness of tree growth; and generates more sampled nodes near the narrow obstacle nodes to make them have a higher probability of passing through the narrow channel. Simulation results show that our algorithm can find feasible paths quickly. Compared with the original RRT* algorithm, the algorithm performs better in terms of time and path cost.