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GA Algorithms in Intelligent Robots

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Fuzzy Evolutionary Computation

Abstract

This chapter presents the role of genetic algorithms in intelligent robots. In general, the motion planning problems in intelligent robots can be fundamentally split into path planning problems, trajectory planning problems, and task planning problems. These planning faculties have many constraints concerning kinematics and dynamics of the robot and therefore it is very difficult to solve these planning problems. This chapter presents the general application of genetic algorithms to these planning tasks. Furthermore, the chapter discusses a trajectory planning problem for redundant manipulators and a motion planning problem for biped locomotion robots.

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Author information

Authors and Affiliations

  1. Department of Micro System Enginering, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, 464-01, Japan

    Toshio Fukuda, Naoyuki Kubota & Takemasa Arakawa

Authors
  1. Toshio Fukuda
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  2. Naoyuki Kubota
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  3. Takemasa Arakawa
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Editor information

Editors and Affiliations

  1. The University of Manitoba, Winnipeg, Manitoba, Canada

    Witold Pedrycz

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© 1997 Springer Science+Business Media New York

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Fukuda, T., Kubota, N., Arakawa, T. (1997). GA Algorithms in Intelligent Robots. In: Pedrycz, W. (eds) Fuzzy Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6135-4_4

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  • DOI: https://doi.org/10.1007/978-1-4615-6135-4_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7811-2

  • Online ISBN: 978-1-4615-6135-4

  • eBook Packages: Springer Book Archive

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