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Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

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Frontiers in Nature-Inspired Industrial Optimization

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

In the last years, Unmanned Aerial Vehicles (UAVs) are being increasingly used in different kinds of inspections due to their maneuverability, flexibility, and efficiency. When deployed in the inspection of large structures, such as dam and slopes, the UAV must cover the largest possible area in the most efficient way. This task is denominated Coverage Path Planning (CPP). Determining the ideal path planning in CPP applications is complex due to dynamic obstacles that cannot be modeled beforehand or when a structural problem is identified, making the UAV to re-plain its trajectory. Therefore, this research proposes the use of bio-inspired metaheuristic techniques for real-time coverage path planning. The methodology dynamically adapts the UAV trajectory when structure failures and obstacles are found. The obtained results showed that the system is efficient, and it has adequate performance for large structures inspections.

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Acknowledgements

The work reported in this paper was performed as part of an interdisciplinary research and development project undertaken by UFJF. The authors acknowledge the financial funding and support of the following companies: CAPES, CNPq, INCT—INERGE, BAESA, ENERCAN, and FOZ DO CHAPECÓ, under supervision of ANEEL—The Brazilian Regulatory Agency of Electricity, through Project number PD 03936-2607/2017. The authors also would like to thank CEFET-RJ.

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Authors and Affiliations

  1. Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Iago Z. Biundini, Aurelio G. Melo, Andre L. M. Marcato & Leonardo M. Honorio

  2. Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro, Brazil

    Milena F. Pinto

Authors
  1. Iago Z. Biundini
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  2. Milena F. Pinto
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  3. Aurelio G. Melo
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  4. Andre L. M. Marcato
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  5. Leonardo M. Honorio
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Corresponding author

Correspondence to Milena F. Pinto .

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Editors and Affiliations

  1. Cross labs, Cross-Compass Ltd, Osaka, Tokyo, Japan

    Mahdi Khosravy

  2. Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta

  3. Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Nilesh Patel

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Biundini, I.Z., Pinto, M.F., Melo, A.G., Marcato, A.L.M., Honorio, L.M. (2022). Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection. In: Khosravy, M., Gupta, N., Patel, N. (eds) Frontiers in Nature-Inspired Industrial Optimization. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3128-3_8

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