Combining Wavefront Propagation and Possibility Theory for Autonomous Navigation in an Indoor Environment

Two major functions in navigation are path planning and obstacle avoidance. A path planner such as the wavefront propagation algorithm can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper reports the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model in which the robot navigates. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments.