Development of an automatic travel system for electric wheelchairs using reinforcement learning systems and CMACs

The existing method for establishing travel routes provides modeled environmental information, but it is difficult to create an environment model for the environments where electric wheelchairs travel because the environment changes constantly due to the existence of moving objects including pedestrians. In this study, we propose an automatic travelling system for an electric wheelchair using reinforcement learning systems and CMACs. We select the best travel route by utilizing these reinforcement learning systems. When a CMAC learns the value function of Q-learning, an improved learning speed is achieved by utilizing the generalizing action. CMACs enable one to reduce the time needed to select the best travel route. Using simulation, a path planning experiment was performed.