A global path planning algorithm based on improved RRT*

Application of the traditional global path planning algorithm RRT* would result in low search efficiency, high memory usage and unsmooth search path. Therefore, this paper proposes a simplified map-based regional sampling RRT* algorithm (SMRS-RRT*) to overcome the abovementioned problems. Firstly, the global grid map is simplified and used to identify the optimal path point set from the starting point to the target point. In addition, intelligent sampling factors are used to expand the guide path for the intelligent sampling area. Then, after iterative search in the intelligent sampling area, an optimized path can be achieved which is a low cost and collision-free path from the starting point to the target point. Finally, based on the path trimming under the minimum turning radius constraint and the B sample curve, a smooth path with continuous curve is generated, thus, the mobile robot can move to the target point quickly, smoothly and safely along the global optimized path. The results of simulation experiments demonstrate that the proposed algorithm can effectively improve the efficiency of the traditional RRT*, speed up the convergence and reduce memory usage. Copyright ©2022 Control and Decision.