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Offline Time-Independent Multi-Agent Path Planning
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This paper studies a novel planning problem for multiple agents that cannot share holding resources, named OTIMAPP (Offline Time-Independent Multi-Agent Path Planning). Given a graph and a set of start-goal pairs, the problem consists in assigning a path to each agent such that every agent eventually reaches their goal without blocking each other, regardless of how the agents are being scheduled at runtime. The motivation stems from the nature of distributed environments that agents take actions fully asynchronous and have no knowledge about those exact timings of other actors. We present solution conditions, computational complexity, solvers, and robotic applications.
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Cited by 1 Pith paper
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Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding
A broad survey of MAPF methods that documents inconsistent evaluation practices and proposes a unified taxonomy.
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