Sampling-Based Path Planning on Configuration-Space Costmaps

This paper addresses path planning considering a cost function defined over the configuration space. The proposed Transition-based RRT planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of RRTs with transition tests used in stochastic optimization methods to accept 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