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'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 greedy Partially Observable Markov Decision Process (POMDP).Benefits of this approach are shown using experimental results in simulation and on our wheelchair platform Sharioto.

Localization based on visible light is a novel technique for indoor positioning that provides a number of advantages over traditional radio frequency based approaches. An important step in the deployment of visible light positioning systems is the calibration procedure, during which environmental parameters such as the positions of light sources are determined. This work presents a proof-of-concept approach to obtain these parameters in an efficient manner by using a mobile robot. This robot builds a map of the environment, and adds the location and identifier of optical transmitters to this map. With this approach, light source modulation frequencies can be estimated with sufficient accuracy to uniquely identify each source. Additionally, the inter-LED distance has an average accuracy of less than 10 cm compared to the real distance.

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