Research on Obstacle Avoidance Path Planning for Robotic Arms Using Improved RRT Algorithm

Abstract In robot path planning, the Rapidly-exploring Random Tree (RRT) algorithm is extensively employed for its effective search performance. However, the conventional RRT algorithm exhibits several limitations, including non-smooth paths, low search efficiency, and insufficient adaptability to dynamic environments. To tackle these issues, this paper introduces an improved RRT algorithm that integrates a target-biased sampling strategy, bidirectional expansion, adaptive step size adjustment, multi-objective optimization, and path smoothing. The target-biased sampling strategy dynamically adjusts the sampling probability, enhancing global exploration capability and accelerating convergence speed. The bidirectional expansion mechanism enables the search tree to expand concurrently from both the start and goal points, thereby substantially decreasing the search time. Adaptive step size adjustment modifies the step size based on the distance to the goal, improving the accuracy of path planning. Additionally, multi-objective optimization, combined with genetic algorithms, optimizes path length, smoothness, and motion time. Path smoothing is achieved through the application of cubic B-splines, which optimize path continuity and minimize unnecessary sharp turns. The experimental outcomes indicate that the enhanced algorithm surpasses conventional RRT and RRT* algorithms in search efficiency, path quality, and computational performance. This makes it a more effective approach for robot path planning in complex and dynamic environments.