Obstacle Avoidance Path Planning for Robotic Arm Based on Improved RRT Algorithm
Keywords:
RRT* Algorithm, RRT Algorithm, Obstacle Avoidance Path Planning, Six-axis Robotic Arm, Sampling Optimization, B-spline CurveAbstract
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.
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Copyright (c) 2026 Zhicheng Wang, Xiaoying He, Jialing Tang, Jianhang Zhang
This work is licensed under a Creative Commons Attribution 4.0 International License.
