Learning human navigational skill for smart wheelchair

In practice, the environments in which mobile robots operate are usually modeled in highly complex geometric representations, and as a result real-time autonomous navigation can be difficult. Such difficulty is even exacerbated for robots with limited but more realistic on-board computational resources since this paradigm of environmental modeling requires enormous computational power. Inspired from human daily life experience, we propose in this paper a new direction for practical robotics navigation system with locally sensed non-geometric environmental modeling. With human-guided demonstrations, the robot can learn and abstract human navigational skill in the form of reactive sensor-motor mapping to navigate in the demonstrated route with simultaneous obstacle avoidance, localization, path and trajectory planning. Learning in a cascade neural network with node-decoupled extended Kalman filtering is adopted as the basis for such reactive mapping. Preliminarily experimental results show the feasibility of this practical approach.