Incorporating learning in motion planning techniques

Abstract

Robot motion planning in a cluttered environment requires knowledge about robot shape and size. These robot characteristics influence system performance even though most motion planning methods do not consider them. This paper presents an ongoing study of general motion planning techniques in combination with knowledge related to robot shape and size. The system acquires knowledge and learns strategies to avoid local collisions and to make global decisions. A neural network is presented that learns local behavior and a learning technique based on a reinforcement method is presented to overcome problems of local minimum.