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ColAG: A Collaborative Air-Ground Framework for Perception-Limited UGVs' Navigation (2310.13324v1)

Published 20 Oct 2023 in cs.RO

Abstract: Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It's challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a $\textit{blind}$ robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perception or SLAM systems. In this paper, we propose a novel system named $\textbf{ColAG}$, to solve the problem of autonomous navigation for a group of $\textit{blind}$ UGVs by introducing cooperation with one UAV, which is the only robot that has full perception capabilities in the group. The UAV uses SLAM for its odometry and mapping while sharing this information with UGVs via limited relative pose estimation. The UGVs plan their trajectories in the received map and predict possible failures caused by the uncertainty of its wheel odometry and unknown risky areas. The UAV dynamically schedules waypoints to prevent UGVs from collisions, formulated as a Vehicle Routing Problem with Time Windows to optimize the UAV's trajectories and minimize time when UGVs have to wait to guarantee safety. We validate our system through extensive simulation with up to 7 UGVs and real-world experiments with 3 UGVs.

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References (28)
  1. Z. Lin and H. H. Liu, “Topology-based distributed optimization for multi-uav cooperative wildfire monitoring,” Optimal Control Applications and Methods, vol. 39, no. 4, pp. 1530–1548, 2018.
  2. A. Pretto, S. Aravecchia, W. Burgard, N. Chebrolu, C. Dornhege, T. Falck, F. Fleckenstein, A. Fontenla, M. Imperoli, R. Khanna et al., “Building an aerial–ground robotics system for precision farming: an adaptable solution,” IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29–49, 2020.
  3. J. Delmerico, E. Mueggler, J. Nitsch, and D. Scaramuzza, “Active autonomous aerial exploration for ground robot path planning,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 664–671, 2017.
  4. I. D. Miller, F. Cladera, T. Smith, C. J. Taylor, and V. Kumar, “Stronger together: Air-ground robotic collaboration using semantics,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9643–9650, 2022.
  5. M. Cognetti, G. Oriolo, P. Peliti, L. Rosa, and P. Stegagno, “Cooperative control of a heterogeneous multi-robot system based on relative localization,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2014, pp. 350–356.
  6. F. Guérin, F. Guinand, J.-F. Brethé, H. Pelvillain et al., “Uav-ugv cooperation for objects transportation in an industrial area,” in 2015 IEEE International Conference on Industrial Technology (ICIT).   IEEE, 2015, pp. 547–552.
  7. E. Olson, “Apriltag: A robust and flexible visual fiducial system,” in 2011 IEEE international conference on robotics and automation.   IEEE, 2011, pp. 3400–3407.
  8. X. Yan, H. Deng, and Q. Quan, “Active infrared coded target design and pose estimation for multiple objects,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 6885–6890.
  9. Z. Xun, J. Huang, Z. Li, C. Xu, F. Gao, and Y. Cao, “Crepes: Cooperative relative pose estimation towards real-world multi-robot systems,” arXiv preprint arXiv:2302.01036, 2023.
  10. L. Wang, D. Cheng, F. Gao, F. Cai, J. Guo, M. Lin, and S. Shen, “A collaborative aerial-ground robotic system for fast exploration,” in Proceedings of the 2018 International Symposium on Experimental Robotics.   Springer, 2020, pp. 59–71.
  11. J. Butzke, A. Dornbush, and M. Likhachev, “3-d exploration with an air-ground robotic system,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2015, pp. 3241–3248.
  12. H. Qin, Z. Meng, W. Meng, X. Chen, H. Sun, F. Lin, and M. H. Ang, “Autonomous exploration and mapping system using heterogeneous uavs and ugvs in gps-denied environments,” IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1339–1350, 2019.
  13. J. He, Y. Zhou, L. Huang, Y. Kong, and H. Cheng, “Ground and aerial collaborative mapping in urban environments,” IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 95–102, 2020.
  14. J. Peterson, H. Chaudhry, K. Abdelatty, J. Bird, and K. Kochersberger, “Online aerial terrain mapping for ground robot navigation,” Sensors, vol. 18, no. 2, p. 630, 2018.
  15. E. Mueggler, M. Faessler, F. Fontana, and D. Scaramuzza, “Aerial-guided navigation of a ground robot among movable obstacles,” in 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (2014).   IEEE, 2014, pp. 1–8.
  16. C. Shen, Y. Zhang, Z. Li, F. Gao, and S. Shen, “Collaborative air-ground target searching in complex environments,” in 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR).   IEEE, 2017, pp. 230–237.
  17. H. Zhu and J. Alonso-Mora, “Chance-constrained collision avoidance for mavs in dynamic environments,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 776–783, 2019.
  18. M. Kamel, J. Alonso-Mora, R. Siegwart, and J. Nieto, “Robust collision avoidance for multiple micro aerial vehicles using nonlinear model predictive control,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 236–243.
  19. S. Patil, J. Van Den Berg, and R. Alterovitz, “Estimating probability of collision for safe motion planning under gaussian motion and sensing uncertainty,” in 2012 IEEE International Conference on Robotics and Automation.   IEEE, 2012, pp. 3238–3244.
  20. B. Zhou, Y. Zhang, X. Chen, and S. Shen, “Fuel: Fast uav exploration using incremental frontier structure and hierarchical planning,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 779–786, 2021.
  21. Z. Meng, H. Qin, Z. Chen, X. Chen, H. Sun, F. Lin, and M. H. Ang, “A two-stage optimized next-view planning framework for 3-d unknown environment exploration, and structural reconstruction,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1680–1687, 2017.
  22. B. Zhou, H. Xu, and S. Shen, “Racer: Rapid collaborative exploration with a decentralized multi-uav system,” IEEE Transactions on Robotics, 2023.
  23. Y. Gao, Y. Wang, X. Zhong, T. Yang, M. Wang, Z. Xu, Y. Wang, Y. Lin, C. Xu, and F. Gao, “Meeting-merging-mission: A multi-robot coordinate framework for large-scale communication-limited exploration,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 13 700–13 707.
  24. E. Stump and N. Michael, “Multi-robot persistent surveillance planning as a vehicle routing problem,” in 2011 IEEE International Conference on Automation Science and Engineering.   IEEE, 2011, pp. 569–575.
  25. X. Zhou, J. Zhu, H. Zhou, C. Xu, and F. Gao, “Ego-swarm: A fully autonomous and decentralized quadrotor swarm system in cluttered environments,” in 2021 IEEE international conference on robotics and automation (ICRA).   IEEE, 2021, pp. 4101–4107.
  26. L. Perron and V. Furnon, “Or-tools,” Google. [Online]. Available: https://developers.google.com/optimization/
  27. F. Kong, X. Liu, B. Tang, J. Lin, Y. Ren, Y. Cai, F. Zhu, N. Chen, and F. Zhang, “Marsim: A light-weight point-realistic simulator for lidar-based uavs,” IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2954–2961, 2023.
  28. W. Xu and F. Zhang, “Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3317–3324, 2021.
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Authors (6)
  1. Zhehan Li (7 papers)
  2. Rui Mao (54 papers)
  3. Nanhe Chen (2 papers)
  4. Chao Xu (283 papers)
  5. Fei Gao (458 papers)
  6. Yanjun Cao (24 papers)
Citations (5)
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