Implementation of IMRRT Path Planning Algorithm for Mobile Robot

Among many robot path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm is widely used due to its strong search ability and easy implementation. However, the RRT algorithm has strong randomness and low search efficiency, and the path generated by the expansion has a certain degree of optimization. In response to this situation, this paper proposes a performance-optimized IMRRT algorithm. Based on the basic RRT algorithm, the expansion weight of the target node is introduced, so that the generation of new nodes in the expanded random tree is biased in the direction of the target node. At the same time, adjust the planned path according to the space environment to improve the problems in the RRT algorithm. The results of experimental simulation show that compared with the basic RRT algorithm, the RRT^* algorithm, and the Informed RRT^* algorithm, the running time of the IMRRT algorithm is reduced by an average of 25.99%, 56.91%, and 53.31%; And the path planning length is also effectively reduced, an average decrease of 4.2%. The IMRRT algorithm improves search efficiency and realizes the optimization of the planned path.