Probabilistic harmonic-function-based method for robot motion planning

This paper presents a robot motion planning method, called PHM, that uses a random sampling scheme together with a potential-field approach based on harmonic functions. The combination of both results in an efficient path planner that is both resolution and probabilistic complete. On one hand, random sampling allows the use of the harmonic functions approach without the explicit knowledge of the robot's Configuration Space. On the other hand, harmonic functions allow an intelligent sampling of Configuration Space by introducing a bias towards the more promising regions.