Obstacle Avoidance Path Planning for Robotic Arm Based on Improved RRT Algorithm - Wang, Zhicheng; He, Xiaoying; Tang, Jialing; Zhang, Jianhang (International Journal of Advanced AI Applications, 2026)
The Rapidly-exploring Random Tree (RRT) algorithm and its variant, RRT*, are commonly used for robotic arm path planning but suffer from high randomness, non-optimal paths, and low efficiency. To address these issues, this paper proposes an improved RRT* algorithm that incorporates a goal-biased sampling strategy and cubic B-spline curve fitting. The method defines and dynamically restricts the search area during tree expansion to improve planning efficiency and goal orientation. Subsequently, cubic B-spline fitting is applied to smooth the path and reduce redundant nodes. Simulation experiments conducted in Python demonstrate that compared to traditional RRT and RRT* algorithms, the proposed approach generates shorter paths with fewer nodes and higher planning success rates, validating its effectiveness for robotic arm obstacle avoidance path planning.