Potential functions-based RH-RRT*
Jiwoo Byeon, Jongho Shin · 2024
In this paper, we propose a potential functions-based RH-RRT* (PRH-RRT*) path planning algorithm to increase the safety and optimality of mobile robot navigation. The RH-RRT* is an extension of the RRT* algorithm that improves search efficiency and reduces computational load by optimizing the local path within a receding horizon. However, the variance during random sampling is challenging as it must balance exploration and path optimality. Additionally, the obstacle collision check method that only considers the robot’s center point, compromises path safety. To address these limits, we propose the PRH-RRT*, which integrates Artificial Potential Fields (APF) with RH-RRT*. The proposed algorithm leverages attractive and repulsive potentials to explore the state space enough, enhancing path safety and optimality by avoiding obstacles and guiding the robot toward the goal. To verify the performance of the proposed method, numerical simulation is conducted and results are analyzed.