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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2009: Next-Generation Applied Intelligence pp 193–201Cite as

An Evolutionary Algorithm with Non-random Initial Population for Path Planning of Manipulators

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

Abstract

In this paper, a hierarchical evolutionary algorithm is proposed for the path planning of manipulators. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The global planner, a MAKLINK based approach, plans a trajectory for a robot end-effector from a starting free-space to goal free-space. An evolutionary algorithm with a non-random initial population is adopted to plan the manipulator configurations along a path given by the former stage. Once the optimal configuration is obtained by the evolutionary algorithm, the optimal chromosomes will be reserved as the initial population. Since the initial population is non-random, the evolution is more efficient and the planned path is smoother than traditional GA. Simulation results show that the proposed algorithm works well, specifically in terms of collision avoidance and computation efficiency.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, No. 123, University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, R.O.C.

    Chien-Chou Lin

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  1. Chien-Chou Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

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© 2009 Springer-Verlag Berlin Heidelberg

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Lin, CC. (2009). An Evolutionary Algorithm with Non-random Initial Population for Path Planning of Manipulators. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_20

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  • DOI: https://doi.org/10.1007/978-3-642-02568-6_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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