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