Multi-Strategy Improved Rapid Random Expansion Tree (RRT) Algorithm for Robotic Arm Path Planning

The purpose of this paper is to propose an improved RRT algorithm that incorporates multiple improvement strategies to solve the problems of low efficiency, long and unsmooth paths in the traditional rapid random expansion tree (RRT) algorithm for path planning of robotic arms. The algorithm first uses a bidirectional tree extension strategy to generate trees from both the starting point and the target position simultaneously, improving search efficiency and reducing redundant paths. Secondly, the algorithm introduces target bias sampling in combination with local Gaussian sampling, which renders the sampling points more focused on the target area, and dynamically adjusts the distribution to improve sampling efficiency and path connection speed. Concurrently, the algorithm is equipped with an adaptive step size strategy, which dynamically adjusts the expansion step size according to the target distance, thereby achieving a balance between rapid expansion over long distances and precise search at close range. Finally, a collision-free operation is ensured by a path verification mechanism, and the path is smoothed using cubic B-splines and minimum curvature optimisation techniques, significantly improving the smoothness of the path and the feasibility of the robot arm movement. As demonstrated by simulation experiments, the improved RRT algorithm exhibits a reduction in the average path length by 18.15%, planning time by 96.29%, the number of nodes by 92.13%, and the number of iterations by 91.60%, in comparison with the conventional RRT algorithm, when operating in complex map mode. These findings substantiate the efficacy and practicality of the improved RRT algorithm in the domain of robotic arm path planning.