Abstract-This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner 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 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. Index Terms-Costmap planning, path quality, sampling-based motion planning.
Prior work such as REF addresses path planning under the consideration of a cost function defined over the configuration space.
Sampling-Based Path Planning on Configuration-Space Costmaps
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Mathematics Computer Science