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A framework for multi-robot coverage analysis of large and complex structures

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Abstract

Coverage analysis is essential for many coverage tasks (e.g., robotic grit-blasting, painting, and surface cleaning) performed by Autonomous Industrial Robots (AIRs). Coverage analysis enables (1) the performance evaluation (e.g., coverage rate and operation efficiency) of AIRs for a coverage task, and (2) the configuration design of a multi-AIR system (e.g., decision on the number of AIRs to be used). Multi-AIR coverage analysis of large and complex structures involves addressing various problems. Thus, a framework is presented in this paper that incorporates various modules (e.g., AIR reachability, AIR base placement, collision avoidance, and area partitioning and allocation) for appropriately addressing the associated problems. The modules within the framework provide the flexibility of utilizing different methods and algorithms, depending on the requirements of the target application. The framework is tested and validated by extensive analyses of 10 different scenarios with up to 10 AIRs.

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Acknowledgements

This work was supported by the Project “Development of Grit-blasting Robots for Commercial Cargo Ship-hull Cleaning”, which was sponsored by China Merchants Heavy Industry (Jiangsu) Co., Ltd (CMHI). A special thanks goes to all team members from CMHI who helped with various aspects of this work.

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Authors and Affiliations

  1. Centre for Autonomous Systems (CAS), University of Technology Sydney, 81 Broadway, Ultimo, NSW, Australia

    Penglei Dai, Mahdi Hassan & Dikai Liu

  2. China Merchants Heavy Industry (Jiangsu) Co., Ltd, No.1, Xin An Jiang Road, Haimen, Jiangsu Province, China

    Xuerong Sun, Ming Zhang & Zhengwei Bian

Authors
  1. Penglei Dai
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  2. Mahdi Hassan
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  3. Xuerong Sun
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  4. Ming Zhang
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  5. Zhengwei Bian
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  6. Dikai Liu
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Correspondence to Penglei Dai.

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Dai, P., Hassan, M., Sun, X. et al. A framework for multi-robot coverage analysis of large and complex structures. J Intell Manuf 33, 1545–1560 (2022). https://doi.org/10.1007/s10845-021-01745-8

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  • DOI: https://doi.org/10.1007/s10845-021-01745-8

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