On-line optimal motion planning for nonholonomic mobile robots

In this paper we propose a novel approach for on-line motion planning of nonholonomic robots through reinforcement learning. The algorithm incorporates a mechanism, the adjoining property, to select the state transitions that will be learned by the robot controller. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as nonholonomic vehicles. Furthermore, a good approximation to the optimal behaviour is obtained by a look-up table without of using function interpolation. Finally, we present both simulation and experimental results to show the satisfactory performance of the method compared with the popular Q-learning algorithm