Tracking Progress in Multi-Agent Path Finding
원문 초록
Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications. Many works appear on this topic each year, and a large number of substantial advancements and performance improvements have been reported. Yet measuring overall progress in MAPF is difficult: there are many potential competitors, and the computational burden for comprehensive experimentation is prohibitively large. Moreover, detailed data from past experimentation is usually unavailable. In this work, we introduce a set of methodological and visualisation tools which can help the community establish clear indicators for state-of-the-art MAPF performance and which can facilitate large-scale comparisons between MAPF solvers. Our objectives are to lower the barrier of entry for new researchers and to further promote the study of MAPF, since progress in the area and the main challenges are made much clearer.
함께 살펴볼 논문
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Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds
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Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning
Graph Coloring to Reduce Computation Time in Prioritized Planning
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search
Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.