A Study on Modeling Error Estimation for Mobile Robot Based on Coevolutionary Computation and Image Processing
Halpage Chinthaka Nuwandika Premachandra*,
Hiroharu Kawanaka*, Tomohiro Yoshikawa**,
Shinji Tsuruoka*, and Tsuyoshi Shinogi*
*Division of Electrical and Electronic Engineering, Graduate School of Engineering, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan
**Dept. of Computational Science and Engineering, Graduate School of Engineering, Nagoya University
Evolutionary Computation (EC) is used to minimize modeling errors between robotic movement in computer simulation and trajectories of an actual robot. Generally, this task is important and so difficult. This paper proposes the method to minimize the modeling error between robotic movements and simulation results using coevolutionary computations with image processing technique. In the proposed method, a video camera on the ceiling captures robot movement, actual robot trajectories are detected from captured images by image processing, and modeling errors are estimated. Results of the experiments using an actual robot confirmed the effectiveness of our proposal and showed that modeling errors are reduced effectively. The sections that follow detail the problems overcome and our projected work.
Hiroharu Kawanaka, Tomohiro Yoshikawa,
Shinji Tsuruoka, and Tsuyoshi Shinogi, “A Study on Modeling Error Estimation for Mobile Robot Based on Coevolutionary Computation and Image Processing,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.7, pp. 825-832, 2007.
-  V. Trianni, S. Nolfi, and M. Dorigo, “Cooperative Hole-Avoidance in a Swarm-Bot,” Robotics & Autonomous Systems, Vol.54 (2), pp. 97-103, 2006.
-  D. Floreano, F. Mondada, A. Perez-Uribe, and D. Roggen, “Machine Self-Evolution,” YLEM journal, Vol.24, No.12, pp. 4-10, 2004.
-  K. Nakaoka, T. Furuhashi, Y. Uchikawa, and H. Maeda, “Remuneration of Fuzzy Classify System and Reliance on Allotment for Learning Development of Large Scale System,” Japan society for Fuzzy theory and intelligent informatics, Vol.8, No.1, pp. 65-72, 1996.
-  T. Yoshikawa, T. Furuhashi, and Y. Uchikawa, “Development of Fuzzy Rules from Genetic Algorithm by Applying DNA Method,” The Institute of Electrical Engineers of Japan, Vol.116-C, No.1, pp. 125-132, 1996.
-  Y. Kawase, H. Yamamoto, T. Furuhashi, M. Matuzaki, and S. Okuma, “A Proposition of Structure Reformation and Compensation on Evolutionary Robotics Modeling Error,” Proc. of the 10th Japan Tokai society for Fuzzy theory, pp. 3-1-3-4, 2001.
-  Y. Masuda, H. Watanabe, and T. Kawaoka, “Position and Posture Measurement of Autonomous Robot Using Image Information,” Proc. of the 20th Annual Conference of the Japanese society for Artificial Intelligence, 1G1-4, 2006.
-  H. Ueno, “A Knowledge-Based Information Modeling for Autonomous Humanoid Service Robot,” IEICE Transactions on Information and Systems, Vol.E85-D, No.4, pp. 657-665, April 2002.
-  C. A. Pena-Reyes and M. Sipper, “Fuzzy CoCo: A Cooperative-Evolutionary Approach to Fuzzy Modeling,” IEEE Transactions on fuzzy Systems, Vol.9, issue:5, pp. 727-737, Oct. 2001.
-  K. S. Byun, C. H. Park, and K. B. Sim, “Co-evolution of Fuzzy Controller for the Mobile Robot Control,” Proc. of the 4th International Symposium on Advanced Intelligent Systems, pp. 82-85, 2003.
-  J. Hatayama, H. Murakoshi, and T. Yamaguchi, “A Movement Instruction System Using Virtual Environment,” Proc. of the 4th International Symposium on Advanced Intelligent Systems, pp. 70-73, 2003.
-  K. Shirasawa, H. Kawanaka, S. Tsuruoka, T. Yoshikawa, and T. Shinogi, “Automatic Determination of an Active Camera View in an Image Based E-Learning System,” Proc. of the 6th International Symposium on Advanced Intelligent Systems, pp. 213-217, 2005.
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