RewardRRT: Path Planning for Multi-Degree-of-Freedom Robots in Narrow Environments

Abstract

A novel path planning algorithm, RewardRRT, is proposed to address the challenge of Multi-degree-of-freedom robot path planning in narrow environments. In this approach, RewardRRT conceptualizes the sampling tree of Rapidly-exploring Random Trees (RRT) as an agent, assigning a reward value function to each sampled state. Simultaneously, the cumulative reward and reward increment are utilized as the state space, with a linear Kalman Filter applied to predict the state transitions. To enhance convergence speed, a bidirectional expansion strategy is implemented, wherein the tree with the lower cumulative reward prediction is prioritized for iteration. Here, the sampling bias is regulated using a sigmoid function and the prediction value. Finally, simulation tests in 4 distinct point cloud scenarios and a real experiment are conducted using a self-designed 21-degree-of-freedom wheeled humanoid robot. Compared to the best-performing algorithm from the Open Motion Planning Library (OMPL) in the same scenarios, RewardRRT achieves improvements in speed by 38.45%, 8.18%, 9.88%, and 14.98%, respectively. Furthermore, RewardRRT exhibits an average planning success rate of 88.25%, surpassing OMPL's best-performing algorithm by 29.75%. These results underscore the effectiveness of RewardRRT in solving path planning challenges in narrow environments.