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.

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.