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Fuzzy logic based collision avoidance for a mobile robot

Published online by Cambridge University Press:  09 March 2009

Angelo Martinez ,
Eddie Tunstel  and
Mo Jamshidi
Angelo Martinez
Affiliation:
CAD Laboratory for Intelligent and Robotic Systems, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 (USA)
Eddie Tunstel
Affiliation:
CAD Laboratory for Intelligent and Robotic Systems, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 (USA)
Mo Jamshidi
Affiliation:
CAD Laboratory for Intelligent and Robotic Systems, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 (USA)
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Summary

Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In general, the problem consists of achieving sensor based motion control of a mobile robot among obstacles in structured and/or unstructured environments with collision-free motion as the priority. A fuzzy logic based intelligent control strategy has been developed here to computationally implement the approximate reasoning necessary for handling the uncertainty inherent in the collision avoidance problem. The fuzzy controller was tested on a mobile robot system in an indoor environment and found to perform satisfactorily despite having crude sensors and minimal sensory feedback.

Keywords

Fuzzy logicCollision avoidanceMobile robotNavigation
Type
Articles
Information
Robotica , Volume 12 , Issue 6 , November 1994 , pp. 521 - 527
Copyright
Copyright © Cambridge University Press 1994

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References

1.Koren, Y. and Borenstein, J., “Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation” 1991 IEEE International Conference on Robotics and Automation,Sacramento, CA,April 1991 (1991) pp. 1398–1404.Google Scholar
2.Borenstein, J. and Koren, Y., “The Vector Field Histogram - Fast Obstacle Avoidance for Mobile Robots” IEEE Transactions on Robotics and Automation 7, No. 3, 278–288 (06, 1991).CrossRefGoogle Scholar
3.Khatib, O., “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots” Int. J. Robotics Research 5, No. 1, 90–98 (1986).CrossRefGoogle Scholar
4.Kuc, R. and Barshan, B., “Navigating Vehicles Through an Unstructured Environment With Sonar” 1989 IEEE International Conference on Robotics and Automation,Scottsdale, AZ,May 1989 (1989) pp. 1422–1426.Google Scholar
5.Moravec, H.P., “Sensor Fusion in Certainty Grids for Mobile Robots” Al Magazine 61–74 (Summer, 1988).CrossRefGoogle Scholar
6.Holenstein, A.A. and Badreddin, E., “Collision Avoidance in a Behavior-Based Mobile Robot Design” 1991 IEEE International Conference on Robotics and Automation,Sacramento, CA,April 1991 (1991) pp. 898–903.Google Scholar
7.Jamshidi, M., Vadiee, N. and Ross, T. (Eds.), Fuzzy Logic and Control - Software and Hardware Applications (Prentice-Hall, Englewood Cliffs, NJ 1993).Google Scholar
8.Lee, C.C., “Fuzzy Logic in Control Systems: Fuzzy Logic Controller - Part I” IEEE Transactions on Systems, Man, and Cybernetics 20, No. 2, 03/04, 1990 (1990) pp. 404–418.CrossRefGoogle Scholar
9.Gat, E., “Robust Low-Computation Sensor-Driven Control for Task-Directed Navigation” 1991 IEEE International Conference on Robotics and Automation,Sacramento, CA,April 1991 (1991) pp. 2484–2489.Google Scholar
10.Tunstel, E., “Autonomous Robot Mapping With Behavior based Navigation” JPL Internal Document, JPL D–10458 (December, 1992).Google Scholar
11.Pin, F.G., Watanabe, H., Symon, J. and Pattay, R.S., “Using Custom-Designed VLSI Fuzzy Inferencing Chips for the Autonomous Navigation of a Mobile Robot” 1992 lEEE/RSJ International Conference on Intelligent Robots and Systems,Raleigh, NC,July 1992 (1992) pp. 790–795.Google Scholar
12.Goodridge, S. and Luo, R.C., “Fuzzy Behavior Fusion for Autonomous Mobile Robot Control” The Second Fuzzy Theory and Technology ConferenceDurham, NC,October 1993 (To appear).Google Scholar
13.Yamakawa, T., “A Fuzzy Inference Engine in Nonlinear Analog Mode and its Application to a Fuzzy Logic Control” IEEE Transactions on Neural Networks 4, No. 3, 496–522 (05, 1993).CrossRefGoogle ScholarPubMed
14.HERO Robot Model £7–18 Technical Manual (Heath Company, Benton Harbor, MI, 1983).Google Scholar
15.Robillard, Mark J., Advanced Robot Systems (Howard W. Sams & Co, Indianapolis, IN, 1984).Google Scholar
16.McComb, G., The Robot Builder's Bonanza (TAB Books, Blue Ridge Summit, PA, 1987).Google Scholar
17.Jones, J.L. and Flynn, A.M., Mobile Robots: Inspiration to Implementation (A K Peters, Wellesley, MA, 1993).Google Scholar
18.Hill, G., Horstkotte, E. and Teichrow, J., Fuzzy-C Development System User's Manual, Release 2.1 (Togai InfraLogic Inc., Irvine, CA, 06 1989).Google Scholar