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Improving Autonomous Exploration Using Reduced Approximated Generalized Voronoi Graphs

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Abstract

Autonomous robotic exploration has been extensively applied in many tasks, such as mobile mapping and indoor searching. One of the most challenging issues is to locate the Next-Best-View and to guide robots through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes an improving method based on reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. Additionally, a fast and robust image thinning algorithm for constructing RAGVGs from metric maps is presented, and an autonomous robotic exploration framework using RAGVGs is designed. The proposed method is validated with three known common data sets and two simulations of autonomous exploration tasks. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and the simulations indicate that the mobile robot controlled by the RAGVG-based exploration method reduced the total time by approximately 20% for the given tasks.

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Acknowledgements

This study is funded by the National Key R&D Program of China (2017YFB0503701) and the National Natural Science Foundation of China (41871298).

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Authors and Affiliations

  1. School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Rd., Wuhan, China

    Lin Li, Xinkai Zuo, Fan Yang, Haihong Zhu, Dalin Li, Jun Liu, Fei Su, Yifan Liang & Gang Zhou

  2. Collaborative Innovation Centre of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China

    Lin Li

  3. The 54th Research Institute of CETC, 589 Zhongshan West Road, Shijiazhuang, 050000, China

    Huixiang Peng

  4. Changsha Intelligent Driving Institute Co., Ltd, 336 Xueshi Road, Changsha, 410006, China

    Dalin Li

Authors
  1. Lin Li
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  2. Xinkai Zuo
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  3. Huixiang Peng
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  4. Fan Yang
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  5. Haihong Zhu
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  6. Dalin Li
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  7. Jun Liu
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  8. Fei Su
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  9. Yifan Liang
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  10. Gang Zhou
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Contributions

This study was completed by the co-authors, and the major experiments and analyses were undertaken by Xinkai Zuo. Lin Li and Haihong Zhu supervised and guided this study. Dalin Li and Fan Yang designed the proposal for the experiments and conducted the analyses. Jun Liu and Yifan Liang aided in the collection and analysis of the data, and Fei Su contributed to algorithm implementation while Xinkai Zuo wrote the paper. Huixiang Peng and Gang Zhou helped a lot to conduct the experiment of the real-world scenario. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Haihong Zhu.

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The authors declare no conflicts of interest.

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Li, L., Zuo, X., Peng, H. et al. Improving Autonomous Exploration Using Reduced Approximated Generalized Voronoi Graphs. J Intell Robot Syst 99, 91–113 (2020). https://doi.org/10.1007/s10846-019-01119-6

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  • DOI: https://doi.org/10.1007/s10846-019-01119-6

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