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A Survivability Enhanced Swarm Robotic Searching System Using Multi-objective Particle Swarm Optimization

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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Abstract

This paper aims at outlining an algorithm for groups of swarm robots solely powered by light energy to survive and complete target searching tasks in unknown fields where light energy charging points and targets are scattered. To sustain the searching operation and solve energy consumption conflicts between surviving and searching, this paper introduces a multi-robot algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and energy-saving decision rules. A novel mechanism of selecting the best performing particle in PSO is introduced. Several sets of simulation experiments were conducted and results show that a 15-robot swarm system running this algorithm is able to search a single target and stabilize the energy level for the long-term simultaneously. It demonstrates the feasibility of applying this energy-optimized MOPSO as a design framework for a long-term searching swarm robot system.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Cheuk Ho Yuen & Kam Tim Woo

Authors
  1. Cheuk Ho Yuen
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  2. Kam Tim Woo
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Corresponding author

Correspondence to Cheuk Ho Yuen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Yuen, C.H., Woo, K.T. (2017). A Survivability Enhanced Swarm Robotic Searching System Using Multi-objective Particle Swarm Optimization. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_18

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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