Research on informed-RRT* algorithm for multi-strategy optimization in mobile robot path planning

Abstract

Path planning is a key technology to realize the autonomous navigation of mobile robots. The Informed-RRT* algorithm is the current path planning algorithm that solves the high sampling efficiency in complex environments, but it also suffers from long planning times and redundant turns in complex environments. For this reason, a multi-strategy optimization Informed-RRT* algorithm is proposed, the first is to introduce the WOA algorithm during the operation of re-selecting the parent node so that the new node can find the selection of the optimal parent node in the search radius, to improve the efficiency of the search, and the second is to select a suitable Bessel curve to interpolate and optimize the generated paths, to make the generated feasible paths smoother and with fewer redundant turns. In the algorithm validation, the McNamee wheeled robot is modeled using the joint simulation platform of ROS and GAZEBO, and the improved Informed RRT* algorithm is encapsulated into the ROS-based path planning algorithm, and the experimental results show that the proposed algorithm outperforms the existing algorithms in terms of the average planning time, the planning length, and the success rate of the planning, and it provides a new feasible solution for the path planning in the complex environment.