The Best Path Planning of Home Care Robot by Building a Conceptual CBR Framework

Wen Huan Lu; Wen Yuan Tao; Jian Guo Wei

doi:10.4028/www.scientific.net/AMM.300-301.556

Paper Titles

Comparison of the On-Line Power Detection and Prediction System
p.537

The Modeling and Simulation of Hexa-Rotor Aerial Robot
p.542

Application of Conductive Rubber Filled by Carbon Black for 3-D Force Measurement
p.547

An On-Line Monitoring System of Temperature Field in Tire
p.551

The Best Path Planning of Home Care Robot by Building a Conceptual CBR Framework
p.556

Design and Analysis of a Non-Vision-Based System for Detecting Unstable Gait
p.561

Application of Mobile Robot System for Cell Production
p.566

Implementation of Sun Tracking for Solar Cell with Maximum Power Point Tracking
p.572

Construct Small-Scale Vertical Axis Wind Turbine Monitoring System by Wireless Sensor Networks
p.576

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 300-301The Best Path Planning of Home Care Robot by...

The Best Path Planning of Home Care Robot by Building a Conceptual CBR Framework

Abstract:

In this research, we present a Case-Based Reasoning framework for planning the best path of navigation robot. That is to adopt the combination of the Case-Based Reasoning method and ontology approach to disambiguate the cases in the context. The details of realizing the best path planning of robot by using this architecture was illustrated. Our approach for realizing case-based reasoning implies the representation of metadata characterizations of the features of cases and semantic formalization of these characterizations.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 300-301)

Pages:

556-560

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.300-301.556

Citation:

Cite this paper

Online since:

February 2013

Authors:

Wen Huan Lu, Wen Yuan Tao, Jian Guo Wei

Keywords:

Case-Based Reasoning (CBR), Ontology, Path Planning

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Albus, J., Outline for a Theory of Intelligence, IEEE Transactions on Systems Man and Cybernetics, Vol. 21, 1991, pp.473-509.

DOI: 10.1109/21.97471

Google Scholar

[2] Gu guochang, Design for the fuzzy part programmer of intelligent AUV, Robot, May (1996).

Google Scholar

[3] Dídac Busquets. A Multiagent Approach to Qualitative Navigation in Robotics. PhD thesis, UPC, (2003).

Google Scholar

[4] Zimmerman, N., Schlenoff, C., and Balakirsky, S., Implementing a Rule-based System to Represent Decision Criteria for On-Road Autonomous Navigation, Proceedings of the 2004 AAAI Spring Symposium on Knowledge Representation and Ontologies for Autonomous Systems, (2004).

DOI: 10.1016/j.robot.2004.07.013

Google Scholar

[5] Maarja Kruusmaa. Global navigation in dynamic environments using case-based reasoning. Auton. Robots, 14(1): 71–91, (2003).

Google Scholar

[6] http: /www. ei. sanken. osaka-u. ac. jp/hozo/eng/index_en. php.

Google Scholar

[7] Maxim Likhachev and Ronald C. Arkin. Spatio-temporal case-based reasoning for behavioral selection. In ICRA, pages 1627–1634, (2001).

DOI: 10.1109/robot.2001.932844

Google Scholar

[8] A. Micarelli, A. Neri, S. Panzieri, and G. Sansonetti. A case-based approach to indoor navigation using sonar maps. In 6th Int. IFAC Symp. On Robot Control SYROCO 2000, pages345–350, (2000).

DOI: 10.1016/s1474-6670(17)37961-2

Google Scholar

[9] http: /www. aiai. ed. ac. uk/links/cbr. html.

Google Scholar