An RRT Path-Planning Approach Based on Artificial Potential Field for Mobile Robots

The traditional RRT (Rapidly Exploring Random Trees) path planning algorithm suffers from issues like excessively high randomness, slow convergence speed, a large number of iterations, and poor path quality. In this paper, we optimize the steps in the path planning process of the RRT algorithm. We achieve this by integrating the potential field method, introducing a dynamic step strategy, eliminating redundant nodes, performing path smoothing, and modifying collision avoidance, thereby enhancing the RRT algorithm. Using the Matlab platform, multiple simulation verifications were performed in both 2D and 3D spaces. Compared with the traditional RRT algorithm and some other improved RRT algorithms in terms of performance indicators such as planning time and iteration times, the results demonstrate that the path planning efficiency, convergence speed, and path quality of the improved RRT algorithm proposed in this paper are significantly improved.