Papers
Topics
Authors
Recent
Search
2000 character limit reached

Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints

Published 26 Feb 2024 in cs.RO | (2402.16690v3)

Abstract: Swarm robotics has garnered significant attention due to its ability to accomplish elaborate and synchronized tasks. Existing methodologies for motion planning of swarm robotic systems mainly encounter difficulties in scalability and safety guarantee. To address these limitations, we propose a Risk-aware swarm mOtion planner using conditional ValuE at Risk (ROVER) that systematically navigates large-scale swarms through cluttered environments while ensuring safety. ROVER formulates a finite-time model predictive control (FTMPC) problem predicated upon the macroscopic state of the robot swarm represented by a Gaussian Mixture Model (GMM) and integrates conditional value-at-risk (CVaR) to ensure collision avoidance. The key component of ROVER is imposing a CVaR constraint on the distribution of the Signed Distance Function between the swarm GMM and obstacles in the FTMPC to enforce collision avoidance. Utilizing the analytical expression of CVaR of a GMM derived in this work, we develop a computationally efficient solution to solve the non-linear constrained FTMPC through sequential linear programming. Simulations and comparisons with representative benchmark approaches demonstrate the effectiveness of ROVER in flexibility, scalability, and risk mitigation.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, vol. 7, pp. 1–41, 2013.
  2. E. Tuci, M. H. Alkilabi, and O. Akanyeti, “Cooperative object transport in multi-robot systems: A review of the state-of-the-art,” Frontiers in Robotics and AI, vol. 5, p. 59, 2018.
  3. R. Groß and M. Dorigo, “Towards group transport by swarms of robots,” International Journal of Bio-Inspired Computation, vol. 1, no. 1-2, pp. 1–13, 2009.
  4. G. Bevacqua, J. Cacace, A. Finzi, and V. Lippiello, “Mixed-initiative planning and execution for multiple drones in search and rescue missions,” in Proceedings of the International Conference on Automated Planning and Scheduling, vol. 25, pp. 315–323, 2015.
  5. K. Cui, M. Li, C. Fabian, and H. Koeppl, “Scalable task-driven robotic swarm control via collision avoidance and learning mean-field control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 1192–1199, IEEE, 2023.
  6. K. Zhang, Z. Yang, and T. Başar, “Multi-agent reinforcement learning: A selective overview of theories and algorithms,” Handbook of reinforcement learning and control, pp. 321–384, 2021.
  7. T. Chu, J. Wang, L. Codecà, and Z. Li, “Multi-agent deep reinforcement learning for large-scale traffic signal control,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1086–1095, 2019.
  8. L. Streichenberg, E. Trevisan, J. J. Chung, R. Siegwart, and J. Alonso-Mora, “Multi-agent path integral control for interaction-aware motion planning in urban canals,” arXiv preprint arXiv:2302.06547, 2023.
  9. M. A. Pereira, A. D. Saravanos, O. So, and E. A. Theodorou, “Decentralized safe multi-agent stochastic optimal control using deep fbsdes and admm,” arXiv preprint arXiv:2202.10658, 2022.
  10. J. Yin, Z. Zhang, E. Theodorou, and P. Tsiotras, “Trajectory distribution control for model predictive path integral control using covariance steering,” in 2022 International Conference on Robotics and Automation (ICRA), pp. 1478–1484, IEEE, 2022.
  11. E. Soria, F. Schiano, and D. Floreano, “Predictive control of aerial swarms in cluttered environments,” Nature Machine Intelligence, vol. 3, no. 6, pp. 545–554, 2021.
  12. E. Soria, F. Schiano, and D. Floreano, “Distributed predictive drone swarms in cluttered environments,” IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 73–80, 2021.
  13. K. Elamvazhuthi and S. Berman, “Mean-field models in swarm robotics: A survey,” Bioinspiration & Biomimetics, vol. 15, no. 1, p. 015001, 2019.
  14. T. Zheng, Q. Han, and H. Lin, “Transporting robotic swarms via mean-field feedback control,” IEEE Transactions on Automatic Control, vol. 67, no. 8, pp. 4170–4177, 2021.
  15. Y. Chen, “Density control of interacting agent systems,” IEEE Transactions on Automatic Control, 2023.
  16. S. Ma, M. Hou, X. Ye, and H. Zhou, “High-dimensional optimal density control with wasserstein metric matching,” in 2023 62nd IEEE Conference on Decision and Control (CDC), pp. 6813–6818, IEEE, 2023.
  17. V. Krishnan and S. Martínez, “Distributed optimal transport for the deployment of swarms,” in 2018 IEEE Conference on Decision and Control (CDC), pp. 4583–4588, IEEE, 2018.
  18. S. Biswal, K. Elamvazhuthi, and S. Berman, “Decentralized control of multiagent systems using local density feedback,” IEEE Transactions on Automatic Control, vol. 67, no. 8, pp. 3920–3932, 2021.
  19. P. Zhu, C. Liu, and S. Ferrari, “Adaptive online distributed optimal control of very-large-scale robotic systems,” IEEE Transactions on Control of Network Systems, vol. 8, no. 2, pp. 678–689, 2021.
  20. A. D. Saravanos, Y. Li, and E. A. Theodorou, “Distributed hierarchical distribution control for very-large-scale clustered multi-agent systems,” arXiv preprint arXiv:2305.18718, 2023.
  21. J. Alonso-Mora, S. Baker, and D. Rus, “Multi-robot formation control and object transport in dynamic environments via constrained optimization,” The International Journal of Robotics Research, vol. 36, no. 9, pp. 1000–1021, 2017.
  22. P. Mao, R. Fu, and Q. Quan, “Optimal virtual tube planning and control for swarm robotics,” arXiv preprint arXiv:2304.11407, 2023.
  23. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram, “Chance-constrained dynamic programming with application to risk-aware robotic space exploration,” Autonomous Robots, vol. 39, pp. 555–571, 2015.
  24. A. Wang, A. Jasour, and B. C. Williams, “Non-gaussian chance-constrained trajectory planning for autonomous vehicles under agent uncertainty,” IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6041–6048, 2020.
  25. D. D. Fan, K. Otsu, Y. Kubo, A. Dixit, J. Burdick, and A.-A. Agha-Mohammadi, “Step: Stochastic traversability evaluation and planning for risk-aware off-road navigation,” in Robotics: Science and Systems, pp. 1–21, RSS Foundation, 2021.
  26. M. Norton, V. Khokhlov, and S. Uryasev, “Calculating cvar and bpoe for common probability distributions with application to portfolio optimization and density estimation,” Annals of Operations Research, vol. 299, pp. 1281–1315, 2021.
  27. A. Hakobyan, G. C. Kim, and I. Yang, “Risk-aware motion planning and control using cvar-constrained optimization,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3924–3931, 2019.
  28. A. Hakobyan and I. Yang, “Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach,” IEEE Transactions on Robotics, 2022.
  29. A. Zolanvari and A. Cherukuri, “Data-driven distributionally robust iterative risk-constrained model predictive control,” in 2022 European Control Conference (ECC), pp. 1578–1583, IEEE, 2022.
  30. American Mathematical Soc., 2021.
  31. Y. Chen, T. T. Georgiou, and A. Tannenbaum, “Optimal transport for gaussian mixture models,” IEEE Access, vol. 7, pp. 6269–6278, 2018.
  32. E. G. Gilbert, D. W. Johnson, and S. S. Keerthi, “A fast procedure for computing the distance between complex objects in three-dimensional space,” IEEE Journal on Robotics and Automation, vol. 4, no. 2, pp. 193–203, 1988.
  33. G. Van Den Bergen, “Proximity queries and penetration depth computation on 3d game objects,” in Game Developers Conference, vol. 170, 2001.
  34. R. T. Rockafellar and S. Uryasev, “Conditional value-at-risk for general loss distributions,” Journal of banking & finance, vol. 26, no. 7, pp. 1443–1471, 2002.
  35. P. Zhu, S. Ferrari, J. Morelli, R. Linares, and B. Doerr, “Scalable gas sensing, mapping, and path planning via decentralized hilbert maps,” Sensors, vol. 19, no. 7, p. 1524, 2019.
  36. X. Yang, H. Gao, P. Zhu, and C. Liu, “Risk-aware motion planning for very-large-scale robotics systems using conditional value-at-risk,” in International Conference on Intelligent Robotics and Applications, pp. 513–525, Springer, 2023.
  37. G. Pertaia and S. Uryasev, “Fitting heavy-tailed mixture models with cvar constraints,” Dependence Modeling, vol. 7, no. 1, pp. 365–374, 2019.
  38. Springer Science & Business Media, 2012.
  39. F. Facchinei, L. Lampariello, and G. Scutari, “Feasible methods for nonconvex nonsmooth problems with applications in green communications,” Mathematical Programming, vol. 164, no. 1-2, pp. 55–90, 2017.

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Tweets

Sign up for free to view the 1 tweet with 0 likes about this paper.

Sign Up for Free

YouTube

Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints 26 views