User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving

Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to provide improved maneuvering, powered wheelchairs have been equipped with sensors, additional computing power and intelligence by various research groups. This paper presents a Bayesian approach to robotic assistance for wheelchair driving, which can be adapted to a specific user. The proposed framework is able to model and estimate even complex maneuvers, and explicitly takes the uncertainty on the user’s intent into account. Besides during intent estimation, user-specific properties and uncertainty on the user’s intent are incorporated when taking assistive actions, such that assistance is tailored to the user’s driving skills. This decision making is modeled as a Partially Observable Markov Decision Process (POMDP). Benefits of this approach are shown using experimental results in simulation and on our wheelchair platform Sharioto.