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Fast-Spanning Ant Colony Optimisation for Mobile Robot Coverage Path Planning

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Advances in Computational Intelligence Systems (UKCI 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1454))

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Abstract

Coverage Path Planning (CPP) aims at finding an optimal path that covers the whole given space. Due to the NP-hard nature, CPP remains a challenging problem. Bio-inspired algorithms such as Ant Colony Optimisation (ACO) have been exploited to solve the problem because they can utilise heuristic information to mitigate the path planning complexity. This paper proposes the Fast-Spanning Ant Colony Optimisation (FaSACO), where ants can explore the environment with various velocities. By doing so, ants with higher velocities can find destinations or obstacles faster and keep lower velocity ants informed by communicating such information via pheromone trails on the path. This mechanism ensures that the (sub-) optimal path is found while reducing the overall path planning time. Experimental results show that FaSACO is 19.3–32.3% more efficient than ACO in terms of CPU time, and re-covers 6.9–12.5% less cells than ACO. This makes FaSACO appealing in real-time and energy-limited applications.

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Acknowledgements

This work was supported by the Manchester Metropolitan University CfACS seed project: Machine Learning for Affordable Mobile Robots.

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Authors and Affiliations

  1. Manchester Metropolitan University, Manchester, M15 6BH, UK

    Christopher Carr & Peng Wang

Authors
  1. Christopher Carr
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  2. Peng Wang
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Corresponding author

Correspondence to Peng Wang .

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Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK

    George Panoutsos

  2. Department of Automatic Control and Systems Engineering, Head of the Intelligent Systems Research Laboratory, The University of Sheffield, Sheffield, South Yorkshire, UK

    Mahdi Mahfouf

  3. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, South Yorkshire, UK

    Lyudmila S Mihaylova

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Carr, C., Wang, P. (2024). Fast-Spanning Ant Colony Optimisation for Mobile Robot Coverage Path Planning. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8_39

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