Priority inheritance with backtracking for iterative multi-agent path finding

Okumura, Keisuke; Machida, Manao; Défago, Xavier; Tamura, Yasumasa

doi:10.1016/j.artint.2022.103752

Cited by 30 publications

(28 citation statements)

References 48 publications

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“…To see the difference in the difficulty of solving instances, we applied an MAPF algorithm to all grid instances above. Specifically, a state-of-the-art suboptimal MAPF algorithm called PIBT + [26] solved all instances at most within 300 ms. The solved instances by PIBT + for MAPF include all detected unsolvable instances by DBS for OTIMAPP.…”

Section: Discussionmentioning

confidence: 99%

“…3) Enhancing each solver: This article presented two basic solvers based on MAPF studies, which is a very active research field. Using state-of-the-art MAPF techniques such as [26], [41], [42], powerful OTIMAPP algorithms are expected to be developed. 4) Applications to other multiagent planning domains: We believe that OTIMAPP can be leveraged for other resource allocation problems with mutual exclusion, e.g., distributed databases [15].…”

Section: Discussionmentioning

confidence: 99%

“…The objective is to find a set of "timed" paths because MAPF assumes that all agents act synchronously. Both optimal (e.g., [18], [38], [39]) and suboptimal (e.g., [25], [26], [40], [41], [42]) algorithms for MAPF have been extensively studied, although these methods rely heavily on timing assumptions and are fragile to action delays in robot execution at runtime. Therefore, many studies on MAPF consider timing uncertainties.…”

Section: B Path Planning For Multiple Robotsmentioning

confidence: 99%

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Offline Time-Independent Multiagent Path Planning

et al. 2023

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This study examines a novel planning problem for multiple agents that cannot share holding resources, named Offline Time-Independent Multiagent Path Planning (OTIMAPP). Given a graph and a set of start-goal pairs, the problem to be addressed is assigning a path to each agent, such that every agent eventually reaches its destination without blocking others, regardless of when each agent starts and finishes each own action. This motivation stems from timing uncertainties, including the reality gaps between planning and robot execution. In contrast to conventional solution, concepts of multirobot path planning that rely on timings, once OTIMAPP solutions are obtained, they can be executed without any synchronization between robot actions. Moreover, there is a theoretical guarantee that all robots eventually reach their destinations, provided they avoid interrobot collisions. This study attempts to establish OTIMAPP both theoretically and practically. Specifically, we present a formalization of the problem, solution conditions based on a categorization of deadlocks, computational complexities showing that OTIMAPP is computationally intractable, practical relaxation of the solution concept, two algorithms to solve OTIMAPP based on multiagent pathfinding algorithms, empirical results showing large OTIMAPP instances can be solved to some extent, as well as robot demonstrations of asynchronous OTIMAPP execution.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: B Path Planning For Multiple Robotsmentioning

confidence: 99%

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Offline Time-Independent Multiagent Path Planning

et al. 2023

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“…There are various one-shot MAPF methods [18], including compilation-based solvers [19], [20], rule-based solvers [21], [22], A*-based solvers [23], [24], and prioritized planning [25], etc. Especially, the search-based solvers [6], [26] and their variants are common.…”

Section: Background and Related Work A One-shot Mapfmentioning

confidence: 99%

“…MAPF is NP-hard to solve optimally [4], [5] on general graphs, planar graphs [6], and even 2D 4-neighbor grids [7]. Recent MAPF solvers include reduction-based [8], [9], [10], [11], rule-based [12], and search-based [13], [14], [15], [16] methods. MAPD: Existing MAPD algorithms [3], [17] decompose a MAPD instance into a sequence of task-assignment and MAPF instances.…”

Section: Introductionmentioning

confidence: 99%

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Tinka

Kiesel

et al. 2021

AAAI

109

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Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.

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Investigation of Integrating Solution Techniques for Lifelong MAPD Problem Considering Endpoints

Matsui

2023

Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection

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Priority inheritance with backtracking for iterative multi-agent path finding

Cited by 30 publications

References 48 publications

Offline Time-Independent Multiagent Path Planning

Offline Time-Independent Multiagent Path Planning

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Investigation of Integrating Solution Techniques for Lifelong MAPD Problem Considering Endpoints

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