Sensory-based motion planning with global proofs

We present DistBug, a new navigation algorithm for mobile robots which exploits range data. The algorithm belongs to the Bug family, which combines local planning with global information that guarantees convergence. Most Bug-type algorithms use contact sensors and consist of two reactive modes of motion: moving toward the target between obstacles and following obstacle boundaries, DistBug uses range data in a new “leaving condition” which allows the robot to abandon obstacle boundaries as soon as global convergence is guaranteed, based on the free range in the direction of the target. The leaving condition is tested directly on the sensor readings, thus making the algorithm simple to implement. To further improve performance, local information is utilized for choosing the boundary following direction, and a search manager is introduced for bounding the search area. The simulation results indicate a significant advantage of DistBug relative to the classical Bug2 algorithm. The algorithm was implemented and tested on a real robot, demonstrating the usefulness and applicability of our approach