Extended Potential Field Method Adam A. Gonthier MEAM 620 Final Project 3/19/2006

Potential Field Approach 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 requiredDrawbacks: 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 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 Parabolic Goal Well 2-D Symmetric Gaussian Obstacle Total Potential Field

Potential Field Algorithm Very simple algorithm Start at initial point Start at initial point Find path opposite to gradient Find path opposite to gradient Set new point at distance  along path from old point Set new point at distance  along path from old point Repeat until goal point is reached Repeat until goal point is reached -Prof. John Spletzer Lehigh University

Potential Field Extension In addition to the goal and obstacle potentials, include rotation and task potentials: Rotation Potential: 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: 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 rotation field helps avoid overturning from an obstacle The task field helps avoid unnecessary turning. The task field helps avoid unnecessary turning. M. Khatib and R. Chatila

Goal and References Goal To produce a Matlab implementation of the extended potential fields method for several configurations To produce a Matlab implementation of the extended potential fields method for several configurations References Prof. John Spletzer: 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), R. W. Beard and T.W. McLain, Motion Planning Using Potential Fields, January 2003