Navigation and Control of a Mobile Robot among Moving Obstacles

In this paper, we address the issues related to navigation and control of a mobile robot which enable it to deal with unexpected moving obstacles by sensor-based control. We propose a probabilistic approach to deal with collision avoidance under uncertainty. We assume that the uncertainties both in the position of a moving obstacle and the error of the measurements is normally distributed. The mean and variance of the distribution are determined by a recursive Kalman filter both from (I priori information and the available sensor data. We define a safe circle for the robot and bound a high-confidence position error ellipse for the moving obstacles. Collisions are avoided by controlling the speed and steering of the mobile robot along the preplanned path to keep the safe circle from intersecting the ellipse. A local robot frame is chosen for collision judgement and avoidance control. The proposed method is suitable for different applications of mobile robots in real world applications and in realtime.