A wall-following method for escaping local minima in potential field based motion planning

Abstract

A wall-following method for escaping local minima encountered by the potential field based motion planning method used in real-time obstacle avoidance is presented. The new algorithm switches to a wall-following control mode when the robot falls into a local minimum. A switches back to the potential field guided control mode when a certain condition is met. A simple switch condition derived from monitoring the distance from the robot's position to the goal position is shown to be effective in escaping local minima in typical laboratory environments. A provision is built into the algorithm, allowing the robot to follow a wall in a different direction if the first attempt fails. The new algorithm is implemented on a Nomad 200 mobile robot. Simulation and experimental results are presented to demonstrate the usefulness of the method.