Sampling-Based Path Planning on

Abstract—This paper addresses path planning to consider a cost function defined over the configuration space. The proposed plan-ner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to ac-cept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed. Index Terms—Costmap planning, path quality, sampling-based motion planning. I