Experimental validation of an online adaptive and learning obstacle avoiding support system for the electric wheelchairs

With the advance of an aging society, people who are physically handicapped have specific needs concerning mobility assistance in relation to their respective living conditions. Moreover, operating an electric wheelchair indoors in confined spaces requires considerable skill. This paper presents an obstacle avoidance support system for an electric wheelchair, using reinforcement learning. The obstacle avoidance is semi-automatically supported by the Minimum Vector Field Histogram (MVFH) method. The MVFH modifies the user manipulation and assists the obstacle avoidance. In the proposed scheme, the modification rate is adjusted by reinforcement learning according to the environment and the user condition. The newly proposed scheme is numerically evaluated on a simulation example. Furthermore, the proposed scheme was applied to an experimental electric wheelchair, and the effectiveness of the proposed technique was verified in a real operating environment.