Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds
SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
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.