179,394 research outputs found
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
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.Comment: Published at AAAI 202
Multi-Goal Multi-Agent Pickup and Delivery
In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem,
where agents constantly engage with new tasks and need to plan collision-free
paths to execute them. To execute a task, an agent needs to visit a pair of
goal locations, consisting of a pickup location and a delivery location. We
propose two variants of an algorithm that assigns a sequence of tasks to each
agent using the anytime algorithm Large Neighborhood Search (LNS) and plans
paths using the Multi-Agent Path Finding (MAPF) algorithm Priority-Based Search
(PBS). LNS-PBS is complete for well-formed MAPD instances, a realistic subclass
of MAPD instances, and empirically more effective than the existing complete
MAPD algorithm CENTRAL. LNS-wPBS provides no completeness guarantee but is
empirically more efficient and stable than LNS-PBS. It scales to thousands of
agents and thousands of tasks in a large warehouse and is empirically more
effective than the existing scalable MAPD algorithm HBH+MLA*. LNS-PBS and
LNS-wPBS also apply to a more general variant of MAPD, namely the Multi-Goal
MAPD (MG-MAPD) problem, where tasks can have different numbers of goal
locations.Comment: IROS 202
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding
The Multi-agent Path Finding (MAPF) problem consists in all agents having to
move to their own destinations while avoiding collisions. In practical
applications to the problem, such as for navigation in an automated warehouse,
MAPF must be solved iteratively. We present here a novel approach to iterative
MAPF, that we call Priority Inheritance with Backtracking (PIBT). PIBT gives a
unique priority to each agent every timestep, so that all movements are
prioritized. Priority inheritance, which aims at dealing effectively with
priority inversion in path adjustment within a small time window, can be
applied iteratively and a backtracking protocol prevents agents from being
stuck. We prove that, regardless of their number, all agents are guaranteed to
reach their destination within finite time, when the environment is a graph
such that all pairs of adjacent nodes belong to a simple cycle of length 3 or
more (e.g., biconnected). Our implementation of PIBT can be fully decentralized
without global communication. Experimental results over various scenarios
confirm that PIBT is adequate both for finding paths in large environments with
many agents, as well as for conveying packages in an automated warehouse.Comment: 8 pages, 2 figures, 2 tables, to be presented at IJCAI-19, Aug 2019,
Maca
GPU-Based Dynamic Search on Adaptive Resolution Grids
This paper presents a GPU-based wave-front propagation technique for multi-agent path planning in extremely large, complex, dynamic environments. Our work proposes an adaptive subdivision of the environment with efficient indexing, update, and neighbor-finding operations on the GPU to address several known limitations in prior work. In particular, an adaptive environment representation reduces the device memory requirements by an order of magnitude which enables for the first time, GPU-based goal path planning in truly large-scale environments (\u3e 2048 m2 ) for hundreds of agents with different targets. We compare our approach to prior work that uses an uniform grid on several challenging navigation benchmarks and report significant memory savings, and up to a 1000X computational speedup
GPU-based dynamic search on adaptive resolution grids
Abstract — This paper presents a GPU-based wave-front propagation technique for multi-agent path planning in ex-tremely large, complex, dynamic environments. Our work proposes an adaptive subdivision of the environment with efficient indexing, update, and neighbor-finding operations on the GPU to address several known limitations in prior work. In particular, an adaptive environment representation reduces the device memory requirements by an order of magnitude which enables for the first time, GPU-based goal path planning in truly large-scale environments (> 2048 m2) for hundreds of agents with different targets. We compare our approach to prior work that uses an uniform grid on several challenging navigation benchmarks and report significant memory savings, and up to a 1000X computational speedup. I
Introducing Delays in Multi-Agent Path Finding
We consider a Multi-Agent Path Finding (MAPF) setting where agents have been
assigned a plan, but during its execution some agents are delayed. Instead of
replanning from scratch when such a delay occurs, we propose delay
introduction, whereby we delay some additional agents so that the remainder of
the plan can be executed safely. We show that the corresponding decision
problem is NP-Complete in general. However, in practice we can find optimal
delay-introductions using CBS for very large numbers of agents, and both
planning time and the resulting length of the plan are comparable, and
sometimes outperform, the state-of-the-art heuristics for replanning. We also
examine the benefits of our method from an explainability point of view.Comment: 10 pages, 8 figures, and 2 table
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