Extended Potential Field Method Adam A. Gonthier MEAM 620 Final Project 3/19/2006
Can use continuous configuration space Therefore, avoids complexities of discrete planning Field is modeled by potential functions Benefits: Local method Does not require global map No prior knowledge of obstacles required Drawbacks: Get caught in local minima Does not find optimal path Obstacles, robot modeled as points; in real world, they are not points Potential Field Approach
Potential Field Approach • Goal is modeled as attractive force • Obstacles modeled as repulsive forces • Robot modeled as point • Robot follows gradient of force field, stopping at goal point 2-D Symmetric Gaussian Obstacle Parabolic Goal Well Total Potential Field
Potential Field Algorithm • Very simple algorithm • Start at initial point • Find path opposite to gradient • Set new point at distance d along path from old point • Repeat until goal point is reached -Prof. John Spletzer Lehigh University
Potential Field Extension M. Khatib and R. Chatila • In addition to the goal and obstacle potentials, include rotation and task potentials: • Rotation Potential: • A potential function for the robot’s orientation relative to the goal is considered, thus forcing the robot along a ‘straighter’ path • Task Potential: • Filter out obstacles that should not influence the robot’s motion; the obstacles that are not in the robot’s path. • These additions should help create a superior path for the robot: • The rotation field helps avoid overturning from an obstacle • The task field helps avoid unnecessary turning.
Goal To produce a Matlab implementation of the extended potential fields method for several configurations References Prof. John Spletzer: http://www.cse.lehigh.edu/%7Espletzer/cse397_Fall05/lec014_MotionPlanning.pdf H. Choset et al (2005). Principles of Robot Motion: Theory, Algorithms and Implementations. Cambridge, MA. MIT Press M. Khatib and R. Chatila. An extended potential field approach for mobile robot sensor-based motions. In Proc. Int. Conf. on Intelligent Autonomous Systems (IAS'4), 1995. R. W. Beard and T.W. McLain, Motion Planning Using Potential Fields, January 2003 Goal and References