Visual navigation and obstacle avoidance using a steering potential function
Humans have a remarkable ability to navigate using only vision, but mobile robots have not been nearly as successful. We propose a new approach to vision-guided local navigation, based upon a model of human navigation. Our approach uses the relative headings to the goal and to obstacles, the distance to the goal, and the angular width of obstacles, to compute a potential field over the robot heading. This potential field controls the angular acceleration of the robot, steering it towards the goal and away from obstacles. Because the steering is controlled directly, this approach is well suited to local navigation for nonholonomic robots. The resulting paths are smooth and have continuous curvature. This approach is designed to be used with single-camera vision without depth information but can also be used with other kinds of sensors. We have implemented and tested our method on a differential-drive robot and present our experimental results.
obstacle avoidance local navigation potential fields vision-based navigation