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STUNS-Planner: a Spatiotemporal Motion Planner with Unbending and Consistency Awareness for Quadrotors in Unknown Environments

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Abstract

Motion planning is fundamental for the autonomous navigation of quadrotors in unknown environments, which often consists of path searching and trajectory generation. However, existing path searching approaches overlook the influence of the tortuosity of the path, and trajectory generation methods usually ignore optimizing the time allocation. They both threaten the security and limit the efficiency of autonomous navigation. In this paper, we propose a SpatioTemporal motion planner with UNbending and conSistent awareness (STUNS-Planner) to generate a safe, feasible and efficient trajectory by reducing the tortuosity of the path and improving time allocation of the trajectory. At the path searching stage, the unbending-aware path searching algorithm is proposed to search a local safe and unbending path before the trajectory generation. At the trajectory generation, we propose a fast scaling time adjustment method that can rapidly improve the efficiency of the trajectory while ensuring feasibility. In addition, to ensure the consistency between sequential replanning trajectories, a consistency-aware trajectory preserving and connecting mechanism is designed. The planner is integrated into a fully autonomous navigation system on a quadrotor. Benchmark comparisons and real-world experiments verify the performance of our system in terms of safety, tortuosity (about 8%-13% lesser) and trajectory time (about 3%-8% shorter) compared with existing approaches.

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The video is available on https://github.com/LTianyyi/stuns-planner/blob/main/stuns-planner.mp4.

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China under Grant 62003176, in part by Tianjin Science Fund for Distinguished Young Scholars under Grant 19JCJQJC62100, in part by the Fundamental Research Funds for the Central Universities, in part by Tianjin Natural Science Foundation under Grant 20JCYBJC01470, in part by Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (Grants MJUKF-IPIC201801), and in part by Industrial Leading Projects of Science and Technology Department of Fujian Province (Grants 2019H0025).

Funding

This work is supported in part by National Natural Science Foundation of China under Grant 62003176, in part by Tianjin Science Fund for Distinguished Young Scholars under Grant 19JCJQJC62100, in part by the Fundamental Research Funds for the Central Universities, in part by Tianjin Natural Science Foundation under Grant 20JCYBJC01470, in part by Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (Grants MJUKF-IPIC201801), and in part by Industrial Leading Projects of Science and Technology Department of Fujian Province (Grants 2019H0025).

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Authors and Affiliations

  1. Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Tianyi Li, Xuebo Zhang & Shiyong Zhang

  2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, 300350, China

    Tianyi Li, Xuebo Zhang & Shiyong Zhang

  3. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Xuetao Zhang

  4. College of Computer and Control Engineering, Minjiang University, Fuzhou, 350108, China

    Xinwei Chen

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Contributions

All authors contributed to the study. Tianyi Li: Conceptualization, methodology, software, validation, data curation, formal analysis, investigation, visualization, and writing - original draft preparation; Xuebo Zhang: Conceptualization, writing - review and editing, funding acquisition, resources, project administration, and supervision; Shiyong Zhang: Methodology, validation, data curation, formal analysis, investigation, and writing - review and editing; Xuetao Zhang: Methodology, writing - review and editing, and resources; Xinwei Chen: writing - review and editing, and funding acquisition.

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Correspondence to Xuebo Zhang.

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Li, T., Zhang, X., Zhang, S. et al. STUNS-Planner: a Spatiotemporal Motion Planner with Unbending and Consistency Awareness for Quadrotors in Unknown Environments. J Intell Robot Syst 107, 7 (2023). https://doi.org/10.1007/s10846-022-01773-3

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