Combining Vision Learning and Interaction for Mobile Robot Path Planning

This paper addresses the question of how to make a robot learn natural terrain selectively and use the knowledge to estimate the terrain for planning an optimal path. A scheme which combines vision learning and interaction is proposed. The vision learning module employs an online boosting learning algorithm to constantly receive and learn the terrain samples each of which comprise the visual features extracted from the sub terrain region image and the traversability measured by the onboard Inertia Measurement Unit (IMU). Using this knowledge, the robot could estimate the new terrains and search for the optimal path to travel using the particle swarm optimization method. To overcome the shortcoming that the robot could not understand the intricate environment exactly, the vision interaction method, which complements the robot's capacity of terrain estimation with the human reasoning ability of path correction, is further applied. Experimental results show the effectiveness of the proposed method.