A navigation scheme with learning for a mobile robot among multiple moving obstacles

Abstract

This paper addresses the problem of navigation for a single mobile robot in an environment of multiple moving obstacles. It presents a near learning algorithm extension to a previously reported (nonlearning) planner. This combined planning system performs the two complementary functions: (1) it derives most feasible velocity of the robot under forecasts for the movements of obstacles in space-time, and (2) it stores obstacle state and feasible velocity values for future reference. Motion planning to derive a feasible velocity of the robot and movement according to the velocity are iterated at regular short intervals. If the robot is confronted with the same situation that is memorized before, the memorized velocity of the robot corresponding to the situation will be used. The algorithm's performance is demonstrated with data from computer simulations.