Learning and transferring human navigational skill to wheelchair

In practice, the environments in which mobile robots operate are usually modelled in highly complex forms, and as a result autonomous navigation can be difficult. A novel navigation learning methodology is presented to abstract and transfer the human sequential navigational skill to a robotic wheelchair by showing the platform how to respond in different local environments along a demonstrated, designated route using a lookup-table representation. This method utilizes limited on-board range sensing information to concisely model local unstructured environments, with respect to the robot, for navigation along the learned route in order to achieve good performance with low on-line computational demand and low-cost hardware requirements. Experimental study demonstrates the feasibility of this method and some interesting characteristics of navigation and its associated localization and environmental modelling problems. Analysis is also conducted to investigate performance evaluation, advantages of the approach, choices of lookup-table inputs and outputs, and potential generalization of this study.