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DREAM: Decentralized Real-time Asynchronous Probabilistic Trajectory Planning for Collision-free Multi-Robot Navigation in Cluttered Environments (2307.15887v2)

Published 29 Jul 2023 in cs.RO

Abstract: Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multi-robot tasks. Dynamic obstacles may also be interactive, i.e., their behavior varies based on the behavior of other entities. We propose a novel representation for interactive behavior of dynamic obstacles and a decentralized real-time multi-robot trajectory planning algorithm allowing inter-robot collision avoidance as well as static and dynamic obstacle avoidance. Our planner simulates the behavior of dynamic obstacles, accounting for interactivity. We account for the perception inaccuracy of static and prediction inaccuracy of dynamic obstacles. We handle asynchronous planning between teammates and message delays, drops, and re-orderings. We evaluate our algorithm in simulations using 25400 random cases and compare it against three state-of-the-art baselines using 2100 random cases. Our algorithm achieves up to 1.68x success rate using as low as 0.28x time in single-robot, and up to 2.15x success rate using as low as 0.36x time in multi-robot cases compared to the best baseline. We implement our planner on real quadrotors to show its real-world applicability.

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References (52)
  1. M. Campbell, M. Egerstedt, J. P. How, and R. M. Murray, “Autonomous driving in urban environments: approaches, lessons and challenges,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 368, no. 1928, pp. 4649–4672, 2010.
  2. B. Li, S. Liu, J. Tang, J.-L. Gaudiot, L. Zhang, and Q. Kong, “Autonomous last-mile delivery vehicles in complex traffic environments,” Computer, vol. 53, no. 11, pp. 26–35, 2020.
  3. R. Inam, K. Raizer, A. Hata, R. Souza, E. Forsman, E. Cao, and S. Wang, “Risk assessment for human-robot collaboration in an automated warehouse scenario,” in International Conference on Emerging Technologies and Factory Automation, vol. 1, 2018, pp. 743–751.
  4. B. Şenbaşlar and G. S. Sukhatme, “Asynchronous real-time decentralized multi-robot trajectory planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 9972–9979.
  5. B. Şenbaşlar, W. Hönig, and N. Ayanian, “RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations,” Autonomous Robots, pp. 1–26, 2023.
  6. B. Şenbaşlar, W. Hönig, and N. Ayanian, “RLSS: Real-time Multi-Robot Trajectory Replanning using Linear Spatial Separations,” 2021. [Online]. Available: https://arxiv.org/abs/2103.07588
  7. J. Tordesillas and J. P. How, “MADER: Trajectory planner in multiagent and dynamic environments,” IEEE Transactions on Robotics, vol. 38, no. 1, pp. 463–476, 2022.
  8. K. Kondo, J. Tordesillas, R. Figueroa, J. Rached, J. Merkel, P. C. Lusk, and J. P. How, “Robust MADER: Decentralized and asynchronous multiagent trajectory planner robust to communication delay,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 1687–1693.
  9. S. Liu, M. Watterson, K. Mohta, K. Sun, S. Bhattacharya, C. J. Taylor, and V. Kumar, “Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1688–1695, 2017.
  10. C. Richter, A. Bry, and N. Roy, “Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments,” in International Symposium of Robotic Research (ISRR), vol. 114, 2013, pp. 649–666.
  11. J. Chen, T. Liu, and S. Shen, “Online generation of collision-free trajectories for quadrotor flight in unknown cluttered environments,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1476–1483.
  12. F. Gao, W. Wu, Y. Lin, and S. Shen, “Online safe trajectory generation for quadrotors using fast marching method and bernstein basis polynomial,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 344–351.
  13. Y. Qi, B. He, R. Wang, L. Wang, and Y. Xu, “Hierarchical motion planning for autonomous vehicles in unstructured dynamic environments,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 496–503, 2023.
  14. B. Luders, M. Kothari, and J. How, “Chance constrained rrt for probabilistic robustness to environmental uncertainty,” in AIAA guidance, navigation, and control conference, 2010, p. 8160.
  15. G. S. Aoude, B. D. Luders, J. M. Joseph, N. Roy, and J. P. How, “Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns,” Autonomous Robots, vol. 35, no. 1, pp. 51–76, 2013.
  16. 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.
  17. G. Chen, S. Wu, M. Shi, W. Dong, H. Zhu, and J. Alonso-Mora, “RAST: risk-aware spatio-temporal safety corridors for mav navigation in dynamic uncertain environments,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 808–815, 2023.
  18. S. H. Nair, E. H. Tseng, and F. Borrelli, “Collision avoidance for dynamic obstacles with uncertain predictions using model predictive control,” in 2022 IEEE 61st Conference on Decision and Control (CDC).   IEEE, 2022, pp. 5267–5272.
  19. L. Janson, E. Schmerling, and M. Pavone, “Monte carlo motion planning for robot trajectory optimization under uncertainty,” in Robotics Research.   Springer, 2018, pp. 343–361.
  20. J. van den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in Robotics Research, 2011, pp. 3–19.
  21. L. Wang, A. D. Ames, and M. Egerstedt, “Safety barrier certificates for collisions-free multirobot systems,” IEEE Transactions on Robotics, vol. 33, no. 3, pp. 661–674, 2017.
  22. B. Riviere, W. Hönig, Y. Yue, and S.-J. Chung, “GLAS: Global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning,” IEEE robotics and automation letters, vol. 5, no. 3, pp. 4249–4256, 2020.
  23. W. Hönig, J. A. Preiss, T. K. S. Kumar, G. S. Sukhatme, and N. Ayanian, “Trajectory planning for quadrotor swarms,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 856–869, 2018.
  24. S. Batra, Z. Huang, A. Petrenko, T. Kumar, A. Molchanov, and G. S. Sukhatme, “Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning,” in 5th Conference on Robot Learning, CoRL 2021, ser. Proceedings of Machine Learning Research, 2021.
  25. M. Damani, Z. Luo, E. Wenzel, and G. Sartoretti, “Primal2: Pathfinding via reinforcement and imitation multi-agent learning-lifelong,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2666–2673, 2021.
  26. Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020, pp. 11 785–11 792.
  27. D. Zhou, Z. Wang, S. Bandyopadhyay, and M. Schwager, “Fast, on-line collision avoidance for dynamic vehicles using buffered voronoi cells,” IEEE RA-L, vol. 2, no. 2, pp. 1047–1054, 2017.
  28. X. Wang, L. Xi, Y. Chen, S. Lai, F. Lin, and B. M. Chen, “Decentralized mpc-based trajectory generation for multiple quadrotors in cluttered environments,” Guidance, Navigation and Control, vol. 01, no. 02, p. 2150007, 2021.
  29. C. E. Luis and A. P. Schoellig, “Trajectory generation for multiagent point-to-point transitions via distributed model predictive control,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 375–382, 2019.
  30. C. Luis, M. Vukosavljev, and A. Schoellig, “Online trajectory generation with distributed model predictive control for multi-robot motion planning,” IEEE Robotics and Automation Letters, vol. PP, pp. 1–1, 2020.
  31. R. M. Murray, M. Rathinam, and W. Sluis, “Differential flatness of mechanical control systems: A catalog of prototype systems,” in ASME International Mechanical Engineering Congress and Exposition.   Citeseer, 1995.
  32. B. Şenbaşlar, W. Hönig, and N. Ayanian, “Robust trajectory execution for multi-robot teams using distributed real-time replanning,” in Distributed Autonomous Robotic Systems (DARS), 2019, pp. 167–181.
  33. J. Park, J. Kim, I. Jang, and H. J. Kim, “Efficient multi-agent trajectory planning with feasibility guarantee using relative bernstein polynomial,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 434–440.
  34. J. Park and H. J. Kim, “Online trajectory planning for multiple quadrotors in dynamic environments using relative safe flight corridor,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 659–666, 2021.
  35. R. T. Farouki, “The bernstein polynomial basis: A centennial retrospective,” Computer Aided Geometric Design, vol. 29, no. 6, pp. 379–419, 2012.
  36. J. Park, D. Kim, G. C. Kim, D. Oh, and H. J. Kim, “Online distributed trajectory planning for quadrotor swarm with feasibility guarantee using linear safe corridor,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4869–4876, 2022.
  37. 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.
  38. C. Toumieh and A. Lambert, “Decentralized multi-agent planning using model predictive control and time-aware safe corridors,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 110–11 117, 2022.
  39. C. Toumieh, “Decentralized multi-agent planning for multirotors: a fully online and communication latency robust approach,” arXiv preprint arXiv:2304.09462, 2023.
  40. J. Wiest, M. Höffken, U. Kreßel, and K. Dietmayer, “Probabilistic trajectory prediction with gaussian mixture models,” in 2012 IEEE Intelligent Vehicles Symposium, 2012, pp. 141–146.
  41. N. Lee, W. Choi, P. Vernaza, C. B. Choy, P. H. Torr, and M. Chandraker, “Desire: Distant future prediction in dynamic scenes with interacting agents,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 336–345.
  42. S. Kim, H. Jeon, J. W. Choi, and D. Kum, “Diverse multiple trajectory prediction using a two-stage prediction network trained with lane loss,” IEEE RA-L, vol. 8, no. 4, pp. 2038–2045, 2023.
  43. F. Bartoli, G. Lisanti, L. Ballan, and A. Del Bimbo, “Context-aware trajectory prediction,” in 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 1941–1946.
  44. Z. Zhou, G. Huang, Z. Su, Y. Li, and W. Hua, “Dynamic attention-based cvae-gan for pedestrian trajectory prediction,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 704–711, 2023.
  45. J. Alonso-Mora, A. Breitenmoser, M. Rufli, P. Beardsley, and R. Siegwart, “Optimal reciprocal collision avoidance for multiple non-holonomic robots,” in Distributed Autonomous Robotic Systems: The 10th International Symposium, 2013, pp. 203–216.
  46. D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in IEEE International Conference on Robotics and Automation (ICRA), 2011, pp. 2520–2525.
  47. R. M. Murray and S. S. Sastry, “Nonholonomic motion planning: steering using sinusoids,” IEEE Transactions on Automatic Control, vol. 38, no. 5, pp. 700–716, 1993.
  48. F. Homm, N. Kaempchen, J. Ota, and D. Burschka, “Efficient occupancy grid computation on the gpu with lidar and radar for road boundary detection,” in IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2010, pp. 1006–1013.
  49. A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An efficient probabilistic 3d mapping framework based on octrees,” Autonomous Robots, vol. 34, no. 3, pp. 189–206, 2013.
  50. S. Edelkamp, S. Jabbar, and A. Lluch-Lafuente, “Cost-algebraic heuristic search,” in AAAI, 2005, pp. 1362–1367.
  51. “GitHub - mit-acl/rmader: Decentralized Multiagent Trajectory Planner Robust to Communication Delay — github.com,” https://github.com/mit-acl/rmader, [Accessed 19-01-2023].
  52. J. A. Preiss, W. Honig, G. S. Sukhatme, and N. Ayanian, “Crazyswarm: A large nano-quadcopter swarm,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 3299–3304.
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Authors (2)
  1. Baskın Şenbaşlar (8 papers)
  2. Gaurav S. Sukhatme (88 papers)
Citations (6)
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