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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.

References

  1. Zhang, X., Chu, Y., Liu, Y., Zhang, X., Zhuang, Y.: A novel informative autonomous exploration strategy with uniform sampling for quadrotors. IEEE Trans. Ind. Electron. 1–1. https://doi.org/10.1109/TIE.2021.3137616(2021)

  2. Wang, H., Zhang, S., Zhang, X., Zhang, X., Liu, J.: Near-optimal 3-D visual coverage for quadrotor unmanned aerial vehicles under photogrammetric constraints. IEEE Trans. Ind. Electron. 69(2), 1694–1704 (2022). https://doi.org/10.1109/TIE.2021.3060643

    Article  Google Scholar 

  3. Kruijff-Korbayová, I., Freda, L., Gianni, M., Ntouskos, V., Hlaváč, V., Kubelka, V., Zimmermann, E., Surmann, H., Dulic, K., Rottner, W., Gissi, E.: Deployment of ground and aerial robots in earthquake-struck Amatrice in Italy (brief report). In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 278–279. https://doi.org/10.1109/SSRR.2016.7784314 (2016)

  4. Zhang, S., Zhang, X., Li, T., Yuan, J., Fang, Y.: Fast active aerial exploration for traversable path finding of ground robots in unknown environments. IEEE Trans. Instrum. Meas. 1–13 (2022). https://doi.org/10.1109/TIM.2022.3158425

  5. Quan, L., Han, L., Zhou, B., Shen, S., Gao, F.: Survey of UAV motion planning. IET Cyber-Syst. Robot. 2(1), 14–21 (2020). https://doi.org/10.1049/iet-csr.2020.0004

    Article  Google Scholar 

  6. Oleynikova, H., Burri, M., Taylor, Z., Nieto, J., Siegwart, R., Galceran, E.: Continuous-time trajectory optimization for online UAV replanning. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5332–5339. https://doi.org/10.1109/IROS.2016.7759784 (2016)

  7. Usenko, V., von Stumberg, L., Pangercic, A., Cremers, D.: Real-Time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 215–222. https://doi.org/10.1109/IROS.2017.8202160 (2017)

  8. Liu, S., Watterson, M., Mohta, K., Sun, K., Bhattacharya, S., Taylor, C.J., Kumar, V.: Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-D complex environments. IEEE Robot. Autom. Lett. 2(3), 1688–1695 (2017). https://doi.org/10.1109/LRA.2017.2663526

    Article  Google Scholar 

  9. Zhou, B., Gao, F., Pan, J., Shen, S.: Robust real-time UAV replanning using guided gradient-based optimization and topological paths. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1208–1214. https://doi.org/10.1109/ICRA40945.2020.9196996 (2020)

  10. Tordesillas, J., Lopez, B.T., How, J.P.: FASTER: fast and safe trajectory planner for flights in unknown environments. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1934–1940. https://doi.org/10.1109/IROS40897.2019.8968021 (2019)

  11. Liu, S., Atanasov, N., Mohta, K., Kumar, V.: Search-based motion planning for quadrotors using linear quadratic minimum time control. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2872–2879. https://doi.org/10.1109/IROS.2017.8206119 (2017)

  12. Ding, W., Gao, W., Wang, K., Shen, S.: An efficient B-spline-based kinodynamic replanning framework for quadrotors. IEEE Trans. Robot. 35(6), 1287–1306 (2019). https://doi.org/10.1109/TRO.2019.2926390

    Article  Google Scholar 

  13. Ryll, M., Ware, J., Carter, J., Roy, N.: Efficient trajectory planning for high speed flight in unknown environments. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 732–738 (2019), https://doi.org/10.1109/ICRA.2019.8793930

  14. Richter, C., Bry, A., Roy, N.: Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments, 649–666. https://doi.org/10.1007/978-3-319-28872-7_37 (2013)

  15. Gao, F., Wu, W., Pan, J., Zhou, B., Shen, S.: Optimal time allocation for quadrotor trajectory generation. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4715–4722. https://doi.org/10.1109/IROS.2018.8593579 (2018)

  16. Foehn, P., Romero, A., Scaramuzza, D.: Time-optimal planning for quadrotor waypoint flight. Sci. Robot. 6(56), 1221 (2021). https://doi.org/10.1126/scirobotics.abh1221

    Article  Google Scholar 

  17. Zhang, X., Wang, J., Fang, Y., Yuan, J.: Multilevel humanlike motion planning for mobile robots in complex indoor environments. IEEE Trans. Autom. Sci. Eng. 16(3), 1244–1258 (2019). https://doi.org/10.1109/TASE.2018.2880245

    Article  Google Scholar 

  18. Wen, J., Zhang, X., Yuan, J., Fang, Y.: E3mop: efficient motion planning based on heuristic-guided motion primitives pruning and path optimization with sparse-banded structure. IEEE Trans. Autom. Sci. Eng. 19 (4), 2762–2775 (2022). https://doi.org/10.1109/TASE.2021.3128521

    Article  Google Scholar 

  19. Shen, P., Zhang, X., Fang, Y., Yuan, M.: Real-time acceleration-continuous path-constrained trajectory planning with built-in tradeoff between cruise and time-optimal motions. IEEE Trans. Autom. Sci. Eng. 17(4), 1911–1924 (2020). https://doi.org/10.1109/TASE.2020.2980423

    Article  Google Scholar 

  20. Harabor, D.D., Grastien, A.: Online graph pruning for pathfinding on grid maps. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. AAAI 2011, San Francisco, California, August 7-11, 2011 (2011)

  21. Mellinger, D., Kumar, V.: Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2520–2525. https://doi.org/10.1109/ICRA.2011.5980409(2011)

  22. Han, L., Gao, F., Zhou, B., Shen, S.: FIESTA: fast incremental euclidean distance fields for online motion planning of aerial robots. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4423–4430 (2019), https://doi.org/10.1109/IROS40897.2019.8968199

  23. Lee, T., Leok, M., McClamroch, N.H.: Geometric tracking control of a quadrotor UAV on SE(3). In: 49th IEEE Conference on Decision and Control (CDC), pp. 5420–5425. https://doi.org/10.1109/CDC.2010.5717652 (2010)

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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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  1. Tianyi Li
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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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