Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Q-ITAGS: Quality-Optimized Spatio-Temporal Heterogeneous Task Allocation with a Time Budget (2404.07902v3)

Published 11 Apr 2024 in cs.MA

Abstract: Complex multi-objective missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning. The associated challenges are exacerbated when solutions to these interconnected problems need to simultaneously maximize task performance and respect practical constraints on time and resources. In this work, we formulate a new class of spatiotemporal heterogeneous task allocation problems that formalize these complexities. We then contribute a novel framework, named Quality-Optimized Incremental Task Allocation Graph Search (Q-ITAGS), to solve such problems. Q-ITAGS offers a flexible interleaved framework that i) explicitly models and optimizes the effect of the collective capabilities on task performance via learnable trait-quality maps, and ii) respects both resource and spatiotemporal constraints including a user-specified time budget (i.e. maximum makespan). In addition to algorithmic contributions, we derive theoretical suboptimality bounds in terms of task performance that varies as a function of a single hyperparameter. Detailed experiments involving a simulated emergency response task and a real-world video game dataset reveal that i) Q-ITAGS results in superior team performance compared to a state-of-the-art method, while also respecting complex spatiotemporal and resource constraints, ii) Q-ITAGS efficiently learns trait-quality maps to enable effective trade-off between task performance and resource constraints, and iii) Q-ITAGS suboptimality bounds consistently hold in practice.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. J. J. Roldán, P. Garcia-Aunon, M. Garzón, J. de León, J. del Cerro, and A. Barrientos, “Heterogeneous multi-robot system for mapping environmental variables of greenhouses,” Sensors, vol. 16, no. 7, 2016.
  2. C. J. McCook and J. M. Esposito, “Flocking for heterogeneous robot swarms: A military convoy scenario,” in Proceedings of the Annual Southeastern Symposium on System Theory, pp. 26–31, 2007.
  3. A. W. Stroupe, T. Huntsberger, B. Kennedy, H. Aghazarian, E. T. Baumgartner, A. Ganino, M. Garrett, A. Okon, M. Robinson, and J. A. Townsend, “Heterogeneous robotic systems for assembly and servicing,” European Space Agency, ESA SP, no. 603, pp. 625–631, 2005.
  4. N. Baras and M. Dasygenis, “An algorithm for routing heterogeneous vehicles in robotized warehouses,” in 5th Panhellenic Conference on Electronics and Telecommunications, 2019.
  5. H. Ravichandar, K. Shaw, and S. Chernova, “STRATA: A unified framework for task assignments in large teams of heterogeneous agents,” Autonomous Agents and Multi-Agent Systems, vol. 34, no. 38, 2020.
  6. D. Matos, P. Costa, J. Lima, and A. Valente, “Multiple Mobile Robots Scheduling Based on Simulated Annealing Algorithm,” in International Conference on Optimization, Learning Algorithms and Applications, 2021.
  7. G. Neville, A. Messing, H. Ravichandar, S. Hutchinson, and S. Chernova, “An Interleaved Approach to Trait-Based Task Allocation and Scheduling,” in International Conference on Intellifent Robots and Systems (IROS), 2021.
  8. A. Messing, G. Neville, S. Chernova, S. Hutchinson, and H. Ravichandar, “GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling,” International Journal of Robotics Research, vol. 41, no. 2, pp. 232–256, 2022.
  9. S. Giordani, M. Lujak, and F. Martinelli, “A distributed multi-agent production planning and scheduling framework for mobile robots,” Computers and Industrial Engineering, vol. 64, no. 1, 2013.
  10. M. Krizmancic, B. Arbanas, T. Petrovic, F. Petric, and S. Bogdan, “Cooperative Aerial-Ground Multi-Robot System for Automated Construction Tasks,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 798–805, 2020.
  11. M. Gombolay, R. Wilcox, and J. Shah, “Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints,” 2016.
  12. P. Schillinger, M. Buerger, and D. Dimarogonas, “Improving Multi-Robot Behavior Using Learning-Based Receding Horizon Task Allocation,” Robotics: Science and Systems, 2018.
  13. B. Fu, W. Smith, D. M. Rizzo, M. Castanier, M. Ghaffari, and K. Barton, “Robust task scheduling for heterogeneous robot teams under capability uncertainty,” IEEE Transactions on Robotics, vol. 39, no. 2, 2022.
  14. A. Srikanthan and H. Ravichandar, “Resource-aware adaptation of heterogeneous strategies for coalition formation,” in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1732–1734, 2022.
  15. A. Prorok, M. A. Hsieh, and V. Kumar, “The Impact of Diversity on Optimal Control Policies for Heterogeneous Robot Swarms,” IEEE Transactions on Robotics, 2017.
  16. S. Mayya, D. S. D’Antonio, D. Saldana, and V. Kumar, “Resilient Task Allocation in Heterogeneous Multi-Robot Systems,” IEEE Robotics and Automation Letters, vol. 6, pp. 1327–1334, 4 2021.
  17. J. Rieskamp, J. R. Busemeyer, and T. Laine, “How do people learn to allocate resources? comparing two learning theories.,” Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 29, no. 6, p. 1066, 2003.
  18. J. Rieskamp and P. E. Otto, “SSL: a theory of how people learn to select strategies.,” Journal of experimental psychology: General, vol. 135, no. 2, p. 207, 2006.
  19. G. Neville, S. Chernova, and H. Ravichandar, “D-ITAGS: a dynamic interleaved approach to resilient task allocation, scheduling, and motion planning,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 1037–1044, 2023.
  20. B. P. Gerkey and M. J. Matarić, “A formal analysis and taxonomy of task allocation in multi-robot systems,” International Journal of Robotics Research, vol. 23, no. 9, 2004.
  21. E. Nunes, M. Manner, H. Mitiche, and M. Gini, “A taxonomy for task allocation problems with temporal and ordering constraints,” Robotics and Autonomous Systems, vol. 90, 2017.
  22. F. Quinton, C. Grand, and C. Lesire, “Market approaches to the multi-robot task allocation problem: a survey,” Journal of Intelligent & Robotic Systems, vol. 107, no. 2, p. 29, 2023.
  23. G. A. Korsah, B. Kannan, B. Browning, A. Stentz, and M. B. Dias, “xBots: An approach to generating and executing optimal multi-robot plans with cross-schedule dependencies,” in International Conference on Robotics and Automation, pp. 115–122, IEEE, 2012.
  24. J. Guerrero, G. Oliver, and O. Valero, “Multi-robot coalitions formation with deadlines: Complexity analysis and solutions,” PLoS ONE, vol. 12, no. 1, 2017.
  25. H. Chakraa, F. Guérin, E. Leclercq, and D. Lefebvre, “Optimization techniques for multi-robot task allocation problems: Review on the state-of-the-art,” Robotics and Autonomous Systems, p. 104492, 2023.
  26. M. Koes, I. Nourbakhsh, and K. Sycara, “Constraint optimization coordination architecture for search and rescue robotics,” in IEEE International Conference on Robotics and Automation, 2006.
  27. G. Neville, H. Ravichandar, K. Shaw, and S. Chernova, “Approximated dynamic trait models for heterogeneous multi-robot teams,” IEEE International Conference on Intelligent Robots and Systems, 2020.
  28. B. Fu, W. Smith, D. Rizzo, M. Castanier, M. Ghaffari, and K. Barton, “Learning task requirements and agent capabilities for multi-agent task allocation,” arXiv preprint arXiv:2211.03286, 2022.
  29. S. Singh, A. Srikanthan, V. Mallampati, and H. Ravichandar, “Concurrent Constrained Optimization of Unknown Rewards for Multi-Robot Task Allocation,” in Proceedings of Robotics: Science and Systems, (Daegu, Republic of Korea), July 2023.
  30. D. Hadfield-Menell, S. Milli, P. Abbeel, S. Russell, and A. D. Dragan, “Inverse Reward Design,” in Conference on Neural Information Processing System, 2017.
  31. P. Mallozzi, R. Pardo, V. Duplessis, P. Pelliccione, and G. Schneider, “MoVEMo: A structured approach for engineering reward functions,” in Proceedings - 2nd IEEE International Conference on Robotic Computing, IRC 2018, vol. 2018-January, pp. 250–257, Institute of Electrical and Electronics Engineers Inc., 4 2018.
  32. D. S. Weld, “An Introduction to Least Commitment Planning,” AI magazine, vol. 15, no. 4, pp. 27–27, 1994.
  33. N. Bhargava and B. Williams, “Multiagent disjunctive temporal networks,” in International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 458–466, 2019.
  34. H. Kitano, S. Tadokoro, I. Noda, H. Matsubara, T. Takahashi, A. Shinjou, and S. Shimada, “RoboCup rescue: search and rescue in large-scale disasters as a domain for autonomous agents research,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 6, pp. 739–743, 1999.
  35. P. Bechon, M. Barbier, G. Infantes, C. Lesire, and V. Vidal, “HiPOP: Hierarchical Partial-Order Planning,” STAIRS, pp. 51–60, 2014.
  36. A. Messing and S. Hutchinson, “Forward Chaining Hierarchical Partial-Order Planning,” International Workshop on the Algorithmic Foundations of Robotics, vol. 14, 2020.
  37. A. Whitbrook, Q. Meng, and P. W. Chung, “A novel distributed scheduling algorithm for time-critical multi-agent systems,” in IEEE International Conference on Intelligent Robots and Systems, vol. 2015-Decem, pp. 6451–6458, 2015.
  38. W. Zhao, Q. Meng, and P. W. Chung, “A Heuristic Distributed Task Allocation Method (PIA),” IEEE Transactions on Cybernetics, vol. 46, pp. 902–915, 4 2016.
  39. M. Staff, “Madden 19 player ratings.” https://www.maddenguides.com/madden-19-player-ratings/, May 2023.
  40. J. Liu and R. K. Williams, “Submodular optimization for coupled task allocation and intermittent deployment problems,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3169–3176, 2019.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now