Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 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

Caching-Augmented Lifelong Multi-Agent Path Finding (2403.13421v3)

Published 20 Mar 2024 in cs.RO, cs.AI, and cs.MA

Abstract: Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to agents as soon as they complete their initial targets, offers a more accurate approximation of real-world warehouse planning. In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF. We have developed a new type of map grid called cache for temporary item storage and replacement, and created a locking mechanism to improve the planning solution's stability. A task assigner (TA) is designed for CAL-MAPF to allocate target locations to agents and control agent status in different situations. CAL-MAPF has been evaluated using various cache replacement policies and input task distributions. We have identified three main factors significantly impacting CAL-MAPF performance through experimentation: suitable input task distribution, high cache hit rate, and smooth traffic. In general, CAL-MAPF has demonstrated potential for performance improvements in certain task distributions, map and agent configurations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. P. R. Wurman, R. D’Andrea, and M. Mountz, “Coordinating hundreds of cooperative, autonomous vehicles in warehouses,” Artificial Intelligence, vol. 29, no. 1, pp. 9–9, 2008.
  2. R. Stern, N. Sturtevant, A. Felner, S. Koenig, H. Ma, T. Walker, J. Li, D. Atzmon, L. Cohen, T. Kumar et al., “Multi-agent pathfinding: Definitions, variants, and benchmarks,” in Proceedings of the International Symposium on Combinatorial Search (SoCS), vol. 10, no. 1, 2019, pp. 151–158.
  3. G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant, “Conflict-based search for optimal multi-agent pathfinding,” Artificial Intelligence, vol. 219, pp. 40–66, 2015.
  4. G. Wagner and H. Choset, “M*: A complete multirobot path planning algorithm with performance bounds,” in 2011 IEEE/RSJ international conference on intelligent robots and systems.   IEEE, 2011, pp. 3260–3267.
  5. M. Barer, G. Sharon, R. Stern, and A. Felner, “Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem,” in Proceedings of the International Symposium on Combinatorial Search (SoCS), 2014.
  6. J. Yu and S. LaValle, “Structure and intractability of optimal multi-robot path planning on graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 27, no. 1, 2013, pp. 1443–1449.
  7. H. Ma, J. Li, T. S. Kumar, and S. Koenig, “Lifelong multi-agent path finding for online pickup and delivery tasks,” in Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2017, p. 837–845.
  8. J. Li, A. Tinka, S. Kiesel, J. W. Durham, T. S. Kumar, and S. Koenig, “Lifelong multi-agent path finding in large-scale warehouses,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 35, no. 13, 2021, pp. 11 272–11 281.
  9. J. Li, Z. Chen, D. Harabor, P. J. Stuckey, and S. Koenig, “Anytime multi-agent path finding via large neighborhood search,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021, pp. 4127–4135.
  10. K. Okumura, M. Machida, X. Défago, and Y. Tamura, “Priority inheritance with backtracking for iterative multi-agent path finding,” Artificial Intelligence, vol. 310, p. 103752, 2022.
  11. K. Okumura, “Lacam: Search-based algorithm for quick multi-agent pathfinding,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 37, no. 10, 2023, pp. 11 655–11 662.
  12. K. R. Gue and R. D. Meller, “Aisle configurations for unit-load warehouses,” IIE Transactions, vol. 41, no. 3, pp. 171–182, 2009.
  13. Y. Zhang, M. C. Fontaine, V. Bhatt, S. Nikolaidis, and J. Li, “Multi-robot coordination and layout design for automated warehousing,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2023, pp. 5503–5511.
  14. Y. Zhang, H. Jiang, V. Bhatt, S. Nikolaidis, and J. Li, “Guidance graph optimization for lifelong multi-agent path finding,” arXiv preprint arXiv:2402.01446, 2024.
  15. C. S. Tang and L. P. Veelenturf, “The strategic role of logistics in the industry 4.0 era,” Transportation Research Part E: Logistics and Transportation Review, vol. 129, pp. 1–11, 2019.
  16. R. Yuan, S. C. Graves, and T. Cezik, “Velocity-based storage assignment in semi-automated storage systems,” Production and Operations Management (POM), vol. 28, no. 2, pp. 354–373, 2019.
  17. X. Li, G. Hua, A. Huang, J.-B. Sheu, T. Cheng, and F. Huang, “Storage assignment policy with awareness of energy consumption in the kiva mobile fulfillment system,” Transportation Research Part E: Logistics and Transportation Review, vol. 144, p. 102158, 2020.
  18. F. Weidinger, N. Boysen, and D. Briskorn, “Storage assignment with rack-moving mobile robots in KIVA warehouses,” Transportation Science (TS), vol. 52, no. 6, pp. 1479–1495, 2018.
  19. D. Silver, “Cooperative pathfinding,” in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), vol. 1, no. 1, 2005, pp. 117–122.
  20. R. J. Luna and K. E. Bekris, “Push and swap: Fast cooperative path-finding with completeness guarantees,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2011, pp. 294–300.
  21. K.-H. C. Wang and A. Botea, “Fast and memory-efficient multi-agent pathfinding,” in Proceedings of the International Conference on Auto- mated Planning and Scheduling (ICAPS), 2008, pp. 380–387.
  22. T. Standley, “Finding optimal solutions to cooperative pathfinding problems,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 24, no. 1, 2010, pp. 173–178.
  23. T. Standley and R. Korf, “Complete algorithms for cooperative pathfinding problems,” in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2011, pp. 668–673.
  24. G. Wagner and H. Choset, “Subdimensional expansion for multirobot path planning,” Artificial intelligence, vol. 219, pp. 1–24, 2015.
  25. J. Li, W. Ruml, and S. Koenig, “Eecbs: A bounded-suboptimal search for multi-agent path finding,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 35, no. 14, 2021, pp. 12 353–12 362.
  26. M. Erdmann and T. Lozano-Perez, “On multiple moving objects,” Algorithmica, vol. 2, pp. 477–521, 1987.
  27. H. Ma, D. Harabor, P. J. Stuckey, J. Li, and S. Koenig, “Searching with consistent prioritization for multi-agent path finding,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 33, no. 01, 2019, pp. 7643–7650.
  28. S.-H. Chan, R. Stern, A. Felner, and S. Koenig, “Greedy priority-based search for suboptimal multi-agent path finding,” in Proceedings of the International Symposium on Combinatorial Search (SoCS), vol. 16, no. 1, 2023, pp. 11–19.
  29. J. Li, Z. Chen, D. Harabor, P. J. Stuckey, and S. Koenig, “Mapf-lns2: fast repairing for multi-agent path finding via large neighborhood search,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 9, 2022, pp. 10 256–10 265.
  30. K. Okumura, “Engineering lacam∗∗{}^{\ast}start_FLOATSUPERSCRIPT ∗ end_FLOATSUPERSCRIPT: Towards real-time, large-scale, and near-optimal multi-agent pathfinding,” arXiv preprint, 2023.
  31. V. Nguyen, P. Obermeier, T. Son, T. Schaub, and W. Yeoh, “Generalized target assignment and path finding using answer set programming,” in Proceedings of the International Symposium on Combinatorial Search (SoCS), vol. 10, no. 1, 2019, pp. 194–195.
  32. M. Čáp, J. Vokřínek, and A. Kleiner, “Complete decentralized method for on-line multi-robot trajectory planning in well-formed infrastructures,” in Proceedings of the International Conference on Auto- mated Planning and Scheduling (ICAPS), vol. 25, 2015, pp. 324–332.
  33. F. Grenouilleau, W.-J. Van Hoeve, and J. N. Hooker, “A multi-label a* algorithm for multi-agent pathfinding,” in Proceedings of the International Conference on Auto- mated Planning and Scheduling (ICAPS), vol. 29, 2019, pp. 181–185.
  34. A. W. Burks, H. H. Goldstine, and J. Von Neumann, “Preliminary discussion of the logical design of an electronic computing instrument,” in The origins of digital computers: Selected papers.   Springer, 1946, pp. 399–413.
  35. J. McCabe, “On serial files with relocatable records,” Operations Research, vol. 13, no. 4, pp. 609–618, 1965.
  36. W. F. K. III, “Analysis of paging algorithms,” in in Proceedings of International Conference on Information Processing (IFIP) Congress, 1971, pp. 485–490.
  37. S. Albers, L. M. Favrholdt, and O. Giel, “On paging with locality of reference,” in ACM Symposium on Theory of Computing (STOC), 2002, pp. 258–267.
  38. A. Dan and D. Towsley, “An approximate analysis of the lru and fifo buffer replacement schemes,” in Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems, 1990, pp. 143–152.
  39. M. Chrobak and J. Noga, “Lru is better than fifo,” Algorithmica, vol. 23, pp. 180–185, 1999.
  40. M. Altınel, C. Bornhövd, S. Krishnamurthy, C. Mohan, H. Pirahesh, and B. Reinwald, “Cache tables: Paving the way for an adaptive database cache,” in Proceedings 2003 VLDB Conference.   Elsevier, 2003, pp. 718–729.
  41. M. Harchol-Balter, T. Leighton, and D. Lewin, “Resource discovery in distributed networks,” in Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing, 1999, pp. 229–237.
  42. J. Gu, M. Goetschalckx, and L. F. McGinnis, “Research on warehouse operation: A comprehensive review,” European Journal of Operational Research (EJOR), vol. 177, no. 1, pp. 1–21, 2007.
  43. K. J. Roodbergen and I. F. A. Vis, “A survey of literature on automated storage and retrieval systems,” European Journal of Operational Research (EJOR), vol. 194, no. 2, pp. 343–362, 2009.
  44. K. Azadeh, R. de Koster, and D. Roy, “Robotized and automated warehouse systems: Review and recent developments,” Transportation Science (TS), vol. 53, no. 4, pp. 917–945, 2019.
  45. B. C. Park, “An optimal dwell point policy for automated storage/retrieval systems with uniformly distributed, rectangular racks,” International journal of production research, vol. 39, no. 7, pp. 1469–1480, 2001.
  46. M. Fukunari and C. Malmborg, “A heuristic travel time model for random storage systems using closest open location load dispatching,” International Journal of Production Research, vol. 46, no. 8, pp. 2215–2228, 2008.
  47. J.-P. Gagliardi, J. Renaud, and A. Ruiz, “On storage assignment policies for unit-load automated storage and retrieval systems,” International Journal of Production Research, vol. 50, no. 3, pp. 879–892, 2012.
  48. Y. Zhang, “Correlated storage assignment strategy to reduce travel distance in order picking,” IFAC-PapersOnLine, vol. 49, no. 2, pp. 30–35, 2016.

Summary

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

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

Tweets