Fuzzy Configuration Space for Moving Obstacle Avoidance of Autonomous Mobile Robots

Jorge Guerra; Hajime Nobuhara; Kaoru Hirota

doi:10.20965/jaciii.2006.p0026

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JACIII Vol.10 No.1 pp. 26-34

doi: 10.20965/jaciii.2006.p0026

(2006)

Paper:

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Fuzzy Configuration Space for Moving Obstacle Avoidance of Autonomous Mobile Robots

Jorge Guerra, Hajime Nobuhara, and Kaoru Hirota

Department of Computational Intelligence and Systems Science,Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:

April 19, 2004

Accepted:

July 29, 2005

Published:

January 20, 2006

Keywords:

mobile robot, fuzzy configuration space, path planning, fuzzy proximity measure, multithreaded models

Abstract

A fuzzy configuration space description method that provides the path planning solution for autonomous mobile robots in dynamically changing environment is proposed based on a hybrid planning algorithm that combines total solutions and reactive control through fuzzy proximity measures. The system (made with C++) that monitors and controls mobile robots remotely is created using a multithreaded model while taking advantage of high performance OpenGL routines to counter the increase in computational cost generated by this approach. Experiments on a real Lego robot are performed using a personal computer with a 1.5GHz Pentium4 CPU and a CCD camera. The efficiency of the hybrid algorithm and the potential of this approach, as a distributed system, in greatly changing dynamic environments are shown. The system provides a starting point for further development of distributed robotic systems, for application in human support tasks where interaction with nonprecise human behaviors are better mentioned with fuzzy parameters.

Cite this article as:

J. Guerra, H. Nobuhara, and K. Hirota, “Fuzzy Configuration Space for Moving Obstacle Avoidance of Autonomous Mobile Robots,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.1, pp. 26-34, 2006.

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References

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[8] A. Saffiotti, “The uses of fuzzy logic in autonomous robot navigation,” Soft computing, No.1, pp. 180-197, 1997.
[9] E. Fabrizi, and A. Saffioti, “Extracting Topology-based maps from gridmaps,” Proc of the IEEE Int. conf. on robotics and automation (ICRA-2000), San Francisco, CA, pp. 2972-2978, April, 2000.
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[14] M. Egerstedt, and X. Hu, “A hybrid control approach to action coordination for mobile robots,” Automatica, Vol.38, pp. 125-130, 2002.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] L. Podsedkowski, J. Nowakowski, M. Idzikowski, and I. Vizvary, “A new solution for path planning in partially known or unknown environment for nonholonomic mobile robots,” Robotics and Autonomous Systems, Vol.34, pp. 145-152, 2001.

[2] [2] J. R. Layne, and K. M. Passino, “Fuzzy Model Reference Learning Control,” Journal of intelligent and Fuzzy systems, Vol.4, No.1, pp. 33-47, 1996.

[3] [3] J. C. Latombe, “Robot motion planning,” Kluwer academic publishers, pp. 12-14, pp. 370-373, 452-459, 464-469, 1996.

[4] [4] D. Driankov, A. Saffiotti, and Editors, “Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Studies in fuzziness and soft computing,” Physica-Verlag, pp. 51-73, 151-179, 235-256, 367-388, 2001.

[5] [5] C. P. Pappis, “Value approximation of fuzzy systems variables,” Fuzzy systems and sets, No.39, pp. 111-115, 1991.

[6] [6] G. Dudek, and M. Jenkin, “Computational principles of mobile robotics,” Cambridge University press, pp. 16-27, 91-100, 121-148, 212-232, 2000.

[7] [7] D. Driankov, R. Palm, and Editors, “Advances in Fuzzy Control, Studies in fuzziness and soft computing,” Physica-Verlag, pp. 103-127, 129-153, 263-281, 1998.

[8] [8] A. Saffiotti, “The uses of fuzzy logic in autonomous robot navigation,” Soft computing, No.1, pp. 180-197, 1997.

[9] [9] E. Fabrizi, and A. Saffioti, “Extracting Topology-based maps from gridmaps,” Proc of the IEEE Int. conf. on robotics and automation (ICRA-2000), San Francisco, CA, pp. 2972-2978, April, 2000.

[10] [10] P. Matsakis, and S. Andrefouet, “The fuzzy line between among and surround,” Proc of the IEEE world congress on computational intelligence, Honolulu, HI, pp. 2564-2570, 2002.

[11] [11] M. Skubic, G. Chronis, P. Matsakis, and J. Keller, “Spatial Relations for Tactical Robot Navigation,” Proc. of the SPIE, Unmanned Ground Vehicle Technology III, Orlando, FL, pp. 346-352, April, 2001.

[12] [12] M. Skubic, G. Chronis, P. Matsakis, and J. Keller, “Generating Linguistic Spatial Descriptions from Sonar Readings Using the Histogram of Forces,” Proc of the IEEE Int. conf. on robotics and automation (ICRA-2001), pp. 485-490, May, 2001.

[13] [13] I. Bloch, and A. Saffioti “On the representation of fuzzy spatial relations in robot maps,” Proc of the 9th Int. conf. on information processing and the management of uncertainty (IPMU-02), Annecy, France, pp. 732-738, July, 2002.

[14] [14] M. Egerstedt, and X. Hu, “A hybrid control approach to action coordination for mobile robots,” Automatica, Vol.38, pp. 125-130, 2002.

[15] [15] Lozano-Perez & Wesley, Configuration Space, 1979.