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INTRODUCTION

Real-time obstacle avoidance is one of the key issues to successful applications of mobile robot systems. All mobile robots feature some kind of collision avoidance, ranging from primitive algorithms that detect an obstacle and stop the robot short of it in order to avoid a collision, through sophisticated algorithms, that enable the robot to detour obstacles. The latter algorithms are much more complex, since they involve not only the detection of an obstacle, but also some kind of quantitative measurements concerning the obstacle's dimensions. Once these have been determined, the obstacle avoidance algorithm needs to steer the robot around the obstacle and resume motion toward the original target. Autonomous navigation represents a higher level of performance, since it applies obstacle avoidance simultaneously with the robot steering toward a given target. Autonomous navigation, in general, assumes an environment with known and unknown obstacles, and it includes global path planning algorithms [3] to plan the robot's path among the known obstacles, as well as local path planning for real-time obstacle avoidance. This article, however, assumes motion in the presence of unknown obstacles, and therefore concentrates only on the local obstacle avoidance aspect