DOI QR코드

DOI QR Code

퍼즐 휴리스틱스: 대규모 환경을 위한 효율적인 다중 에이전트 경로 탐색 알고리즘

Puzzle Heuristics: Efficient Lifelong Multi-Agent Pathfinding Algorithm for Large-scale Challenging Environments

  • 이원종 ;
  • 심준열 ;
  • 남창주
  • Wonjong Lee (Dept. of Artificial Intelligence, Sogang University) ;
  • Joonyeol Sim (Dept. of Electronic Engineering, Sogang University) ;
  • Changjoo Nam (Dept. of Electronic Engineering, Sogang University)
  • 투고 : 2024.04.19
  • 심사 : 2024.07.23
  • 발행 : 2024.08.30

초록

This paper describes the solution method of Team AIRLAB used to participate in the League of Robot Runners Competition which tackles the problem of Lifelong Multi-agent Pathfinding (MAPF). In lifelong MAPF, multiple agents are tasked to navigate to their respective goal locations where new goals are consecutively revealed once they reach initial goals. The agents need to avoid collisions and deadlock situations while they navigate to perform tasks. Our method consists of (i) Puzzle Heuristics, (ii) MAPF-LNS2, and (iii) RHCR. The Puzzle Heuristics is our own algorithm that generates a compact heuristic table contributing to reduce memory consumption and computation time. MAPF-LNS2 and RHCR are state-of-the-art algorithms for MAPF. By combining these three algorithms, our method can improve the efficiency of paths for all agents significantly.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1C1C1008476).

참고문헌

  1. R. Stern, N. Sturtevant, A. Felner, S. Koenig, H. Ma, T. Walker, J. Li, D. Atzmon, L. Cohen, T. K. Kumar, R. Bartak, and E. Boyarski, "Multi-agent pathfinding: Definitions, variants, and benchmarks," International Symposium on Combinatorial Search, vol. 10, no. 1, pp. 151-158, 2019, DOI: 10.1609/socs.v10i1.18510. 
  2. H. Ma, J. Li, T. K. S. Kumar, and S. Koenig, "Lifelong multi-agent path finding for online pickup and delivery tasks," arXiv:1705. 10868, 2017, DOI: 10.48550/arXiv.1705.10868. 
  3. The League of Robot Runners, [Online], https://www.leagueofrobotrunners.org, Accessed: Aug. 30, 2023. 
  4. J. Li, Z. Chen, D. Harabor, P. J. Stuckey, and S. Koenig, "MAPF-LNS2: Fast repairing for multi-agent pathfinding via large neighborhood search," AAAI Conference on Artificial Intelligence, vol. 36, no. 9, pp. 10256-10265, 2022, DOI: 10.1609/aaai.v36i9.21266. 
  5. J. Li, A. Tinka, S. Kiesel, J. W. Durham, T. K. S. Kumar, and S. Koenig, "Lifelong multi-agent path finding in large-scale warehouses," AAAI Conference on Artificial Intelligence, vol. 35, no. 13, pp. 11272-11281, 2021, DOI: 10.1609/aaai.v35i13.17344. 
  6. P. Shaw, "Using constraint programming and local search methods to solve vehicle routing problems," International Conference on Principles and Practice of Constraint Programming, pp. 417-431, 1998, DOI: 10.1007/3-540-49481-2_30. 
  7. M. Phillips and M. Likhachev, "SIPP: Safe interval path planning for dynamic environments," 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, pp. 5628-5635, 2011, DOI: 10.1109/ICRA.2011.5980306. 
  8. D. Silver, "Cooperative pathfinding," AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 1, no. 1, pp. 117-122, 2005, DOI: 10.1609/aiide.v1i1.18726.