Multimodal control of a robotic wheelchair: Using contextual information for usability improvement

In this paper, a method to perform semi-autonomous navigation on a wheelchair is presented. The wheelchair could be controlled in semi-autonomous mode estimating the user's intention by using a face pose recognition system or in manual mode. The estimator was performed within a Bayesian network approach. To switch these two modes, a speech interface was used. The user's intention was modeled as a set of typical destinations visited by the user. The algorithm was implemented to one experimental wheelchair robot. The new application of the wheelchair system with more natural and easy-to-use human machine interfaces was one of the main contributions. as user's habits and points of interest are employed to infer the user's desired destination in a map. Erroneous steering signals coming from the user-machine interface input are filtered out, improving the overall performance of the system. Human aware navigation, path planning and obstacle avoidance are performed by the robotic wheelchair while the user is just concerned with “looking where he wants to go”.