Path planning of robot arm based on improved RRT* algorithm

Abstract

In the field of robotic arm path planning, traditional path planning algorithms face issues such as low computational efficiency, poor path quality, and imprecise collision detection. These problems become especially pronounced in complex, multi-obstacle environments, where the performance of existing algorithms fails to meet the requirements of real-time performance and accuracy. To address these challenges, this paper proposes an improved RRT algorithm based on quantized sampling and spatial optimization techniques. By combining quantized sampling strategies with spatial partitioning methods such as quadtrees and octrees, the algorithm optimizes sampling point selection and collision detection mechanisms, significantly enhancing both path planning efficiency and accuracy. Experimental results demonstrate that the improved algorithm shows significant improvements in path length, path generation time, and node count compared to traditional RRT algorithms in both 2D and 3D complex environments. Overall, the proposed improved RRT algorithm excels in enhancing path planning quality and efficiency, providing a more efficient and robust solution for robotic arm path planning in complex environments.