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A GPU-Enabled Parallel Genetic Algorithm for Path Planning of Robotic Operators

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GPU Computing and Applications

Abstract

Genetic algorithm (GA) is a class of global optimization algorithm inspired by the Darwinian biological evolution. It is widely applied in the field of robotic path planning. Parallel GA (PGA) is a subclass of GA which is able to achieve good solutions in a short time. This chapter discusses the utilization of a PGA in determining collision-free path for robotic operators. GPU-style genetic operators are designed to speed up the GA process while improving the quality of solutions. GPU parallelization for a master–slave parallel GA (MSPGA) is implemented by parallelizing the selection, crossover and mutation operators.

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Acknowledgements

The authors would like to express their sincere thanks to those who help this work in one way or another.

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Authors and Affiliations

  1. School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Panpan Cai & Indhumathi Chandrasekaran

  2. Nanyang Technological University, Singapore, Singapore

    Yiyu Cai

  3. Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore

    Yiyu Cai

  4. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Jianmin Zheng

Authors
  1. Panpan Cai
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  2. Yiyu Cai
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  3. Indhumathi Chandrasekaran
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  4. Jianmin Zheng
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Corresponding author

Correspondence to Yiyu Cai .

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Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Yiyu Cai

  2. Nvidia, Singapore, Singapore

    Simon See

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Cai, P., Cai, Y., Chandrasekaran, I., Zheng, J. (2015). A GPU-Enabled Parallel Genetic Algorithm for Path Planning of Robotic Operators. In: Cai, Y., See, S. (eds) GPU Computing and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-287-134-3_1

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  • DOI: https://doi.org/10.1007/978-981-287-134-3_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-133-6

  • Online ISBN: 978-981-287-134-3

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