NDLI: Learning Human Navigational Skill for Smart Wheelchair in a Static Cluttered Route

Content Provider	IEEE Xplore Digital Library
Author	Chow, H.N. Yangsheng Xu
Copyright Year	1982
Abstract	In practice, the environments in which mobile robots operate are usually modeled in highly complex forms and, as a result, autonomous navigation and localization can be difficult. The difficulties are exacerbated for practical robots with limited on-board computational resources and complex planning algorithms, since this paradigm of environmental modeling requires enormous computational power. A novel navigation/localization 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, static cluttered 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 or localization 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, localization, and environmental modeling 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 paper
Sponsorship	IEEE Industrial Electronics Society
Starting Page	1350
Ending Page	1361
Page Count	12
File Size	475946
File Format	PDF
ISSN	02780046
Volume Number	53
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Navigation Wheelchairs Mobile robots Computational modeling Robot sensing systems Table lookup Robotics and automation Design for experiments Hardware smart wheelchair Environmental modeling human-skill modeling learning by demonstration localization navigation
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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