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Impact of Environmental Changes on Optimized Robotics Collective Motion for Multi-objective Coverage Tasks

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Evolutionary Multi-Criterion Optimization (EMO 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15512))

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Abstract

Coverage tasks have garnered significant interest in recent years due to their applications in fields such as environmental monitoring, search and rescue, and robotic exploration. Swarm robotics has emerged as a promising approach to tackle these challenges, leveraging collective behaviors to enhance efficiency. However, the effectiveness of swarm algorithms often hinges on the careful tuning of parameters, which can be influenced by varying environmental features. This study investigates the impact of these features on the performance of optimized collective motion in swarm robotics for solving multi-objective coverage problems. Through a comprehensive sensitivity analysis, we evaluate how changes in parameters-such as arena size, number of robots, obstacle density, and obstacle structure-affect swarm performance in solving coverage problems. The analysis focuses on key performance metrics across multiple objectives: coverage percentage, coverage time, robot connectivity, and collision rates. Our findings provide critical insights into the adaptability of swarm algorithms, highlighting the importance of environmental context in optimizing swarm efficacy across competing objectives. These results contribute to the development of more robust and adaptive swarm-based solutions for multi-objective coverage tasks.

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Acknowledgement

The authors would like to thank UNSW for the financial support provided through the UIPA scholarship program. This research was supported by resources from the National Computational Infrastructure (NCI Australia), funded by the Australian Government through NCRIS.

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Authors and Affiliations

  1. School of Systems and Computing, University of New South Wales, Canberra, Australia

    Reda Ghanem, Ismail M. Ali & Kathryn Kasmarik

  2. School of Engineering and Technology, University of New South Wales, Canberra, Australia

    Matthew Garratt

Authors
  1. Reda Ghanem
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  2. Ismail M. Ali
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  3. Kathryn Kasmarik
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  4. Matthew Garratt
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Corresponding author

Correspondence to Reda Ghanem .

Editor information

Editors and Affiliations

  1. University of New South Wales, Canberra, NSW, Australia

    Hemant Singh

  2. University of New South Wales, Canberra, NSW, Australia

    Tapabrata Ray

  3. SLB Cambridge Research, Cambridge, UK

    Joshua Knowles

  4. RMIT, Melbourne, VIC, Australia

    Xiaodong Li

  5. University of Warwick, Coventry, Warwickshire, UK

    Juergen Branke

  6. University of New South Wales, Canberra, NSW, Australia

    Bing Wang

  7. Japan Aerospace Exploration Agency, Tokyo, Japan

    Akira Oyama

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Ghanem, R., Ali, I.M., Kasmarik, K., Garratt, M. (2025). Impact of Environmental Changes on Optimized Robotics Collective Motion for Multi-objective Coverage Tasks. In: Singh, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2025. Lecture Notes in Computer Science, vol 15512. Springer, Singapore. https://doi.org/10.1007/978-981-96-3506-1_21

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  • DOI: https://doi.org/10.1007/978-981-96-3506-1_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-96-3505-4

  • Online ISBN: 978-981-96-3506-1

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