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Swarm Robotics Obstacle Avoidance: A Progressive Minimal Criteria Novelty Search-Based Approach

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Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

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Abstract

Swarm robots are required to explore and search large areas. In order to cover largest possible area while keeping communications, robots try to maintain hexagonal formation while moving. Obstacle avoidance is an extremely important task for swarm robotics as it saves robots from hitting objects and being damaged.

This paper introduces novelty search evolutionary algorithm to swarm robots multi-objective obstacle avoidance problem in order to overcome deception and reach better solutions.

This work could teach robots how to move in different environments with 2.5% obstacles coverage while keeping their connectivity more than 82%. Percentage of robots reached the goal was more than 97% in 70% of the environments and more than 90% in the rest of the environments.

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References

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Author information

Authors and Affiliations

  1. Computer and Systems Department Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Nesma M. Rezk, Yousra Alkabani, Hassan Bedour & Sherif Hammad

Authors
  1. Nesma M. Rezk
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  2. Yousra Alkabani
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  3. Hassan Bedour
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  4. Sherif Hammad
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Corresponding author

Correspondence to Nesma M. Rezk .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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© 2015 Springer International Publishing Switzerland

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Rezk, N.M., Alkabani, Y., Bedour, H., Hammad, S. (2015). Swarm Robotics Obstacle Avoidance: A Progressive Minimal Criteria Novelty Search-Based Approach. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_49

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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