Path planning for mobile robots using an improved reinforcement learning scheme

The current method for establishing travel routes provides modeled environmental information. However, it is difficult to create an environment model for the environments in which mobile robots travel because the environment changes constantly due to the existence of moving objects, including pedestrians. In this study, we propose a path planning system for mobile robots using reinforcement-learning systems and Cerebellar Model Articulation Controllers (CMACs). We select the best travel route utilizing these reinforcement-learning systems. When a CMAC learns the value function of Q-Learning, it improves learning speed by utilizing generalizing action. CMACs enable us to reduce the time needed to select the best travel route. Using simulation and real robots, we perform a path-planning experiment. We report the results of simulation and experiment on traveling by on-line learning.