Path Planning Algorithm for IB-RRT* Robotic Arms Based on Optimal Sampling and Sparse Nodes

In this paper, we propose an improved OSSN-IBRRT* algorithm for path planning of robotic arms to address issues such as uneven sampling, low planning efficiency, and node redundancy encountered in the IB-RRT* algorithm in complex environments. To enhance the effectiveness of exploration in the spatial domain, we utilize low-discrepancy sequence sampling instead of pseudo-random sequence sampling generated by computers for the selection of sampling points. To improve the efficiency of path planning, we introduce path iterations within an ellipsoid space. Additionally, we tackle challenges arising from strong bias leading to node clustering and local minima in narrow spaces by incorporating a node filtering mechanism during the generation of new nodes. For obstacle avoidance in complex environments, we employ an envelope-based approach to transform obstacles into regular objects. By employing a laser radar for environment scanning and generating a dense point cloud, we convert it into an octree grid map for obstacle avoidance. Simulation results show that under the same number of iterations, the improved algorithm can get a better path, and the time is shorter.