Improved BiRRT* with Artificial Potential Field-Based Sampling Point and Catmull Roll Interpolation in Robot Path Planning

Abstract

Traditional RRT-based path planning for an unmanned ground vehicle (UGV) is highly influenced by random sample point generation, number of inflexions towards the target and search efficiency to find the shortest path through the obstacle environment. To deal with these challenges, we propose a path planning method as an enhanced bi-directional-rapidly exploring random tree (RRT) * to handle the shortcomings of the traditional algorithms. It uses the artificial potential field (APF) to identify obstacle space and employ non-uniform sample points accordingly towards the goal point. It improves path generation ability and obstacle avoidance, resulting in faster convergence and fewer inflexion points. For managing system dynamics, the generated pathways are additionally refined by the use of Catmull-Rom Spline Interpolation, producing smoother planned trajectories. Simulation shows that our enhanced bi-directional RRT* algorithm outperforms traditional methods to generate the shortest path while avoiding obstacles.