Papers
Topics
Authors
Recent
Search
2000 character limit reached

Iterative Planning for Multi-agent Systems: An Application in Energy-Aware UAV-UGV Cooperative Task Site Assignments

Published 16 Jan 2024 in eess.SY and cs.SY | (2401.08846v2)

Abstract: This paper presents an iterative planning framework for multi-agent systems with hybrid state spaces. The framework uses transition systems to mathematically represent planning tasks and employs multiple solvers to iteratively improve the plan until computation resources are exhausted. When integrating different solvers for iterative planning, we establish theoretical guarantees on the mathematical framework to ensure recursive feasibility. The proposed framework enables continual improvement of solution optimality, efficiently using allocated computation resources. The proposed method is validated by applying it to an energy-aware UGV-UAV cooperative task site assignment. The results demonstrate the continual solution improvement while preserving real-time implementation ability compared to algorithms proposed in the literature.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. O. Thakoor, J. Garg, and R. Nagi, “Multiagent UAV routing: A game theory analysis with tight price of anarchy bounds,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 1, pp. 100–116, 2020.
  2. J. G. Carlsson and S. Song, “Coordinated logistics with a truck and a drone,” Management Science, vol. 64, no. 9, p. 4052–4069, 2018.
  3. Yordanova, H. D. Griffiths, and S. Hailes, “Rendezvous planning for multiple autonomous underwater vehicles using a Markov decision process,” IET Radar Sonar and Navigation, vol. 11, pp. 1762–1769, 2017.
  4. J. Xin, C. Meng, A. D’Ariano, F. Schulte, J. Peng, and R. R. Negenborn, “Energy-efficient routing of a multirobot station: A flexible time-space network approach,” IEEE Transactions on Automation Science and Engineering, pp. 1–15, 2022.
  5. R. M. A. Swaszek and C. G. Cassandras, “Receding horizon control for station inventory management in a bike-sharing system,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 1, pp. 407–417, 2020.
  6. Z. Fang and J. Mao, “Energy-efficient elevating transfer vehicle routing for automated multi-level material handling systems,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1107–1123, 2020.
  7. S. G. Manyam, K. Sundar, and D. W. Casbeer, “Cooperative routing for an air–ground vehicle team—exact algorithm, transformation method, and heuristics,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 1, pp. 537–547, 2020.
  8. S. Ramasamy, J.-P. F. Reddinger, J. M. Dotterweich, M. A. Childers, and P. A. Bhounsule, “Cooperative route planning of multiple fuel-constrained unmanned aerial vehicles with recharging on an unmanned ground vehicle,” in 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 155–164.
  9. ——, “Coordinated route planning of multiple fuel-constrained unmanned aerial systems with recharging on an unmanned ground vehicle for mission coverage,” Journal of Intelligent and Robotic Systems, vol. 106, no. 1, 2022.
  10. M. Ostertag, N. Atanasov, and T. Rosing, “Trajectory planning and optimization for minimizing uncertainty in persistent monitoring applications,” Journal of Intelligent and Robotic Systems, vol. 106, no. 1, 2022.
  11. J. Redding, T. Toksoz, N. K. Ure, A. Geramifard, J. P. How, M. A. Vavrina, and J. Vian, “Distributed multi-agent persistent surveillance and tracking with health management,” in Proceedings of the AIAA Guidance Navigation and Control Conference, Portland, OR, USA, 2011, pp. 8–11.
  12. D. Mitchell, N. Chakraborty, K. P. Sycara, and N. Michael, “Multi-robot persistent coverage with stochastic task costs,” 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3401–3406, 2015.
  13. N. Mathew, S. L. Smith, and S. L. Waslander, “Planning paths for package delivery in heterogeneous multirobot teams,” IEEE Transactions on Automation Science and Engineering, vol. 12, pp. 1298–1308, 2015.
  14. K. Chour, J.-P. Reddinger, J. Dotterweich, M. Childers, J. Humann, S. Rathinam, and S. Darbha, “A reactive energy-aware rendezvous planning approach for multi-vehicle teams,” in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2022, pp. 537–542.
  15. K. Yu, A. K. Budhiraja, S. Buebel, and P. Tokekar, “Algorithms and experiments on routing of unmanned aerial vehicles with mobile recharging stations,” Journal of Field Robotics, vol. 36, pp. 602–616, 2018.
  16. L. Vu, D. M. Vu, M. H. Hà, and V.-P. Nguyen, “The two-echelon routing problem with truck and drones,” International Transactions in Operational Research, vol. 29, no. 5, pp. 2968–2994, 2022. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/itor.13052
  17. H. Zhou, H. Qin, C. Cheng, and L.-M. Rousseau, “An exact algorithm for the two-echelon vehicle routing problem with drones,” Transportation Research Part B: Methodological, vol. 168, pp. 124–150, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0191261523000024
  18. P. Maini, K. Sundar, M. Singh, S. Rathinam, and P. B. Sujit, “Cooperative aerial–ground vehicle route planning with fuel constraints for coverage applications,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, pp. 3016–3028, 2019.
  19. M. Kenzin, I. V. Bychkov, and N. Maksimkin, “Coordinated recharging of heterogeneous mobile robot teams during continuous large scale missions,” 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT), vol. 1, pp. 745–750, 2020.
  20. M. B. Rappaport and C. Bettstetter, “Coordinated recharging of mobile robots during exploration,” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6809–6816, 2017.
  21. Z. Xing, W. Wu, H. Niu, and R. Hu, “Battery charge dispatching in multi-robot systems,” 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), vol. 1, pp. 1–6, 2021.
  22. 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, 2011, pp. 569–575.
  23. K. Sundar, S. Venkatachalam, and S. Rathinam, “Formulations and algorithms for the multiple depot, fuel-constrained, multiple vehicle routing problem,” 2016 American Control Conference (ACC), pp. 6489–6494, 2015.
  24. X. Wen and G. Wu, “Heterogeneous multi-drone routing problem for parcel delivery,” Transportation Research Part C: Emerging Technologies, vol. 141, p. 103763, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X22001966
  25. S. Teng, X. Hu, P. Deng, B. Li, Y. Li, Y. Ai, D. Yang, L. Li, Z. Xuanyuan, F. Zhu, and L. Chen, “Motion planning for autonomous driving: The state of the art and future perspectives,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 6, pp. 3692–3711, 2023.
  26. L. Dong, Z. He, C. Song, and C. Sun, “A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures,” Journal of Systems Engineering and Electronics, vol. 34, no. 2, pp. 439–459, 2023.
  27. S. Gu, G. Chen, L. Zhang, J. Hou, Y. Hu, and A. Knoll, “Constrained reinforcement learning for vehicle motion planning with topological reachability analysis,” Robotics, vol. 11, no. 4, 2022. [Online]. Available: https://www.mdpi.com/2218-6581/11/4/81
  28. C. Belta, B. Yordanov, and E. A. Gol, “Formal methods for discrete-time dynamical systems.” [Online]. Available: https://link.springer.com/book/10.1007/978-3-319-50763-7#about-this-book
  29. L. Zhen, J. Gao, Z. Tan, S. Wang, and R. Baldacci, “Branch-price-and-cut for trucks and drones cooperative delivery,” IISE Transactions, vol. 55, pp. 271–287, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:249440594
  30. M.-A. Coindreau, O. Gallay, and N. Zufferey, “Parcel delivery cost minimization with time window constraints using trucks and drones,” Networks, vol. 78, no. 4, pp. 400–420, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/net.22019
  31. S. Ramasamy, M. S. Mondal, J.-P. F. Reddinger, J. M. Dotterweich, J. D. Humann, M. A. Childers, and P. A. Bhounsule, “Heterogenous vehicle routing: comparing parameter tuning using genetic algorithm and bayesian optimization,” in 2022 International Conference on Unmanned Aircraft Systems (ICUAS), 2022, pp. 104–113.
  32. S. W. Cone, “Casualty evacuation optimization in a conflicted environment.” [Online]. Available: https://apps.dtic.mil/sti/citations/trecms/AD1200424
  33. K. Marler, R. Yoshida, and C. Vogiatzis, “US Marine Corps rapid planning and logistics routing against uncertainty,” Naval Engineers Journal, vol. 135, no. 1, pp. 115–125, 2023. [Online]. Available: https://www.ingentaconnect.com/content/asne/nej/2023/00000135/00000001/art00022
  34. N. Mathew, S. L. Smith, and S. L. Waslander, “A graph-based approach to multi-robot rendezvous for recharging in persistent tasks,” 2013 IEEE International Conference on Robotics and Automation, pp. 3497–3502, 2013.
  35. R. Zhang, Q. Zong, X. Zhang, L. Dou, and B. Tian, “Game of drones: Multi-UAV pursuit-evasion game with online motion planning by deep reinforcement learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7900–7909, 2023.
  36. S. Zimmermann, G. Hakimifard, M. Zamora, R. Poranne, and S. Coros, “A multi-level optimization framework for simultaneous grasping and motion planning,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2966–2972, 2020.
  37. C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated task and motion planning,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 4, no. 1, pp. 265–293, 2021. [Online]. Available: https://doi.org/10.1146/annurev-control-091420-084139
  38. P. A. Abdulla, “Well (and better) quasi-ordered transition systems,” The Bulletin of Symbolic Logic, vol. 16, no. 4, p. 457–515, 2010.
  39. Y. Cao, G. Chen, and E. E. Kerre, “Bisimulations for fuzzy-transition systems,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 3, pp. 540–552, 2011.
  40. Y. Cao, S. X. Sun, H. Wang, and G. Chen, “A behavioral distance for fuzzy-transition systems,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 4, pp. 735–747, 2013.
  41. A. A. Julius and G. J. Pappas, “Approximations of stochastic hybrid systems,” IEEE Transactions on Automatic Control, vol. 54, no. 6, pp. 1193–1203, 2009.
  42. A. Girard, A. A. Julius, and G. J. Pappas, “Approximate simulation relations for hybrid systems,” Discrete Event Dynamic Systems, vol. 18, no. 2, p. 163–179, Oct 2007.
  43. S. Coogan and M. Arcak, “Efficient finite abstraction of mixed monotone systems,” in Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control, ser. HSCC ’15.   New York, NY, USA: Association for Computing Machinery, 2015, p. 58–67. [Online]. Available: https://doi.org/10.1145/2728606.2728607
  44. P. Naumov and J. Tao, “Coalition power in epistemic transition systems,” in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, ser. AAMAS ’17.   Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2017, p. 723–731.
Citations (3)
View on Semantic Scholar

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

Collections

Sign up for free to add this paper to one or more collections.

Sign Up