Bayesian plan recognition and shared control under uncertainty: assisting wheelchair drivers by tracking fine motion paths

The last years have witnessed a significant increase in the percentage of old and disabled people. Members of this population group very often require extensive help for performing daily tasks like moving around or grasping objects. Unfortunately, assistive technology is not always available to people needing it. For instance, steering a wheelchair can represent an extremely fatiguing or simply impossible task to many elderly or disabled users. Most of the existing assistance platforms try to help users without considering their specific needs. However, driving performance may vary considerably across users due to different pathologies or just due to temporary effects like fatigue. Therefore, we propose in this paper a user adapted shared control approach aimed at helping users in driving a power wheelchair. Adaption to the user is achieved by estimating the user's true intent out of potentially noisy steering signals before assisting him/her. The user's driving performance is explicitly modeled in order to recognize the user's intention or plan together with the uncertainty on it. Safe navigation is achieved by merging the potentially noisy input of the user with fine motion trajectories computed online by a 3D planner. Encouraging results on assisting a user who cannot steer to the left are reported on K.U.Leuven's intelligent wheelchair Sharioto.