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Artificial Immune Algorithm Based Obstacle Avoiding Path Planning of Mobile Robots

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

This investigation studies the applicability of using mobile robots with artificial immune algorithm (AIA) based obstacle-avoiding path planning inside a specified environment in real time. Path planning is an important problem in robotics. AIA is applied to determine the position and the angle between a mobile robot, an obstacle and the goal in a limited field. The method seeks to find the optimal path. The objectives are to minimize the length of the path and the number of turns. The results of the real-time experiments present the effectiveness of the proposed method.

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References

  1. Liu, S., Tian, Y., Liu, J.: Multi mobile robot path planning based on genetic algorithm. IEEE Trans. on Intelligent Control and Automation 5, 4706–4709 (2004)

    Google Scholar 

  2. Zhang, Y., Wu, C.-D., Li, M.-X.: Rough set and genetic algorithm in path planning of robot. IEEE Trans. on Machine Learning and Cybernetics 2, 698–701 (2003)

    Google Scholar 

  3. Kawanaka, H., Yoshikawa, T., Tsuruoka, S.: Acquisition of Fuzzy Control Rules for a Mobile Robot Using Genetic Algorithm. IEEE Trans on Advanced Motion Control, 507–512 (2000)

    Google Scholar 

  4. Jerne, N.K.: The immune system. Scientific American 229(1), 52–60 (1973)

    Article  Google Scholar 

  5. Jerne, N.K.: Idiotypic networks and other preconceived ideas. Immunological Rev. 79, 5–24 (1984)

    Article  Google Scholar 

  6. Gu, D., Hu, H., Reynolds, J., Tsang, E.: GA-based Learning in Behaviour Based Robotics. IEEE Trans. on Computational Intelligence in Robotics and Automation 3, 1521–1526 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Electronic Engineering, Lunghwa University of Science and Technology, No.300, Sec. 1, Wanshou Rd., Guishan Shiang, Taoyuan County, 333, Taiwan

    Yen-Nien Wang, Hao-Hsuan Hsu & Chun-Cheng Lin

Authors
  1. Yen-Nien Wang
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  2. Hao-Hsuan Hsu
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  3. Chun-Cheng Lin
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, YN., Hsu, HH., Lin, CC. (2005). Artificial Immune Algorithm Based Obstacle Avoiding Path Planning of Mobile Robots. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_120

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  • DOI: https://doi.org/10.1007/11539117_120

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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