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1. A Hierarchical Approach to Multi-Agent Path Finding

   Zhang, Han; Yao, Mingze; Liu, Ziang; Li, Jiaoyang; Terr, Lucas; Chan, Shao-Hung; Kumar, T. K.; Koenig, Sven (January 2021, International Symposium on Combinatorial Search)

   null (Ed.)

   The Multi-Agent Path Finding (MAPF) problem arises in many real-world applications, ranging from automated warehousing to multi-drone delivery. Solving the MAPF problem optimally is NP-hard, and existing optimal and bounded-suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP solves as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps. 

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2. Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities

   https://doi.org/10.1609/socs.v17i1.31565  

   Jiang, He; Zhang, Yulun; Veerapaneni, Rishi; Li, Jiaoyang (June 2024, Proceedings of the International Symposium on Combinatorial Search)

   Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning approach to the 2023 League of Robot Runners LMAPF competition, which leads us to several interesting research challenges and future directions. In this paper, we outline three main research challenges. The first challenge is to search for high-quality LMAPF solutions within a limited planning time (e.g., 1s per step) for a large number of agents (e.g., 10,000) or extremely high agent density (e.g., 97.7%). We present future directions such as developing more competitive rule-based and anytime MAPF algorithms and parallelizing state-of-the-art MAPF algorithms. The second challenge is to alleviate congestion and the effect of myopic behaviors in LMAPF algorithms. We present future directions, such as developing moving guidance and traffic rules to reduce congestion, incorporating future prediction and real-time search, and determining the optimal agent number. The third challenge is to bridge the gaps between the LMAPF models used in the literature and real-world applications. We present future directions, such as dealing with more realistic kinodynamic models, execution uncertainty, and evolving systems. 

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3. Load Balancing in Distributed Multi-Agent Path Finder (DMAPF)

   Pianpak, Poom; Li, Jiaoyang; Tran Cao, Son (May 2023, Engineering Multi-Agent Systems - 11th International EMAS Workshop - Revised Selected Papers)

   Andrei Ciortea; Mehdi Dastani; Jieting Luo (Ed.)

   The Multi-Agent Path Finding (MAPF) is a problem of finding a plan for agents to reach their desired locations without colliding. Distributed Multi-Agent Path Finder (DMAPF) solves the MAPF problem by decomposing a given MAPF problem instance into smaller subproblems and solve them in parallel. DMAPF works in rounds. Between two consecutive rounds, agents may migrate between two adjacent subproblems following their abstract plans, which are pre-computed, until all of them reach the areas that contain their desired locations. Previous works on DMAPF compute an abstract plan for each agent without the knowledge of other agents’ abstract plans, resulting in high congestion in some areas, especially those that act as corridors. The congestion negatively impacts the runtime of DMAPF and prevents it from being able to solve dense MAPF problems. In this paper, we (i) investigate the use of Uniform-Cost Search to mitigate the congestion. Additionally, we explore the use of several other techniques including (ii) using timeout estimation to preemptively stop solving and relax a subproblem when it is likely to get stuck; (iii) allowing a solving process to manage multiple subproblems – aimed to increase concurrency; and (iv) integrating with MAPF solvers from the Conflict-Based Search family. Experimental results show that our new system is several times faster than the previous ones; can solve larger and denser problems that were unsolvable before; and has better runtime than PBS and EECBS, which are state-of-the-art centralized suboptimal MAPF solvers, in problems with a large number of agents. 

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4. Probabilistic Robust Multi-Agent Path Finding

   Atzmon, Dor; Li, Jiaoyang; Felner, Ariel; Nachmani, Eliran; Shperberg, Shahaf S.; Sturtevant, Nathan; Koenig, Sven (June 2020, Proceedings of the International Conference on Automated Planning and Scheduling)

   In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur. We propose two methods for verifying that given solutions are p-robust. We also introduce an optimal CBS-based algorithm, called pR-CBS, and a fast suboptimal algorithm, called pR-GCBS, for finding such solutions. Our experiments show that a p-robust solution reduces the number of conflicts compared to optimal, non-robust solutions. 

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5. Probabilistic Robust Multi-Agent Finding

   Atzmon, S.; Stern, R.; Felner, A.; Sturtevant, N.; Koenig, S. (January 2020, International Conference on Automated Planning and Scheduling (ICAPS))

   In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur. We propose two methods for verifying that given solutions are p-robust. We also introduce an optimal CBS-based algorithm, called pR-CBS, and a fast suboptimal algorithm, called pR-GCBS, for finding such solutions. Our experiments show that a p-robust solution reduces the number of conflicts compared to optimal, non-robust solutions. 

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