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In Multiagent Path Finding (MAPF), the goal is to compute efficient, collision-free paths for multiple agents navigating a network from their sources to targets, minimizing the schedule's makespan-the total time until all agents reach their…

多智能体系统 · 计算机科学 2025-08-07 Foivos Fioravantes , Dušan Knop , Nikolaos Melissinos , Michal Opler

In multi-agent path finding (MAPF) the task is to navigate agents from their starting positions to given individual goals. The problem takes place in an undirected graph whose vertices represent positions and edges define the topology.…

多智能体系统 · 计算机科学 2019-07-31 Pavel Surynek , T. K. Satish Kumar , Sven Koenig

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-world scenarios and four…

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,…

机器人学 · 计算机科学 2024-04-23 Justin Kottinger , Tzvika Geft , Shaull Almagor , Oren Salzman , Morteza Lahijanian

Multi-Agent Path Finding (MAPF) is a problem of finding a sequence of movements for agents to reach their assigned location without collision. Centralized algorithms usually give optimal solutions, but have difficulties to scale without…

多智能体系统 · 计算机科学 2021-09-20 Poom Pianpak , Tran Cao Son

Typical Multi-agent Path Finding (MAPF) solvers assume that agents move synchronously, thus neglecting the reality gap in timing assumptions, e.g., delays caused by an imperfect execution of asynchronous moves. So far, two policies enforce…

多智能体系统 · 计算机科学 2020-12-15 Keisuke Okumura , Yasumasa Tamura , Xavier Défago

Multi-Agent Combinatorial Path Finding (MCPF) seeks collision-free paths for multiple agents from their initial locations to destinations, visiting a set of intermediate target locations in the middle of the paths, while minimizing the sum…

机器人学 · 计算机科学 2024-09-17 Yuanhang Zhang , Xuemian Wu , Hesheng Wang , Zhongqiang Ren

Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents…

人工智能 · 计算机科学 2021-05-11 Pavel Surynek

Multi-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through…

多智能体系统 · 计算机科学 2023-06-30 Kazumi Kasaura , Ryo Yonetani , Mai Nishimura

MAPF problem aims to find plans for multiple agents in an environment within a given time, such that the agents do not collide with each other or obstacles. Motivated by the execution and monitoring of these plans, we study Dynamic MAPF…

人工智能 · 计算机科学 2026-01-14 Aysu Bogatarkan , Esra Erdem

We study a graph pathfinding problem Distance-$r$ Independent Unlabeled Multi-Agent Pathfinding, finding a set of collision-free paths between two sets where agents must stay at pairwise distance at least $r+1$ at all times. This additional…

多智能体系统 · 计算机科学 2026-05-13 Takahiro Suzuki , Yuma Tamura , Keisuke Okumura

The Mutliagent Path Finding (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destinations as soon as possible, but without colliding with each…

计算复杂性 · 计算机科学 2025-06-03 Foivos Fioravantes , Dušan Knop , Jan Matyáš Křišťan , Nikolaos Melissinos , Michal Opler , Tung Anh Vu

We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the set of agents is confined to a graph, a set of goal vertices is given and each of these vertices has to be reached by some agent. The problem is to find an…

人工智能 · 计算机科学 2024-01-30 Zain Alabedeen Ali , Konstantin Yakovlev

Purpose of Review Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called Multi-Agent Path Finding (MAPF). This review surveys…

机器人学 · 计算机科学 2022-06-24 Hang Ma

Multi-agent path finding (MAPF) is the problem of finding paths for multiple agents such that they do not collide. This problem manifests in numerous real-world applications such as controlling transportation robots in automated warehouses,…

人工智能 · 计算机科学 2024-06-18 Carmel Shabalin , Omri Kaduri , Roni Stern

We explore the use of Artificial Potential Fields (APFs) to solve Multi-Agent Path Finding (MAPF) and Lifelong MAPF (LMAPF) problems. In MAPF, a team of agents must move to their goal locations without collisions, whereas in LMAPF, new…

人工智能 · 计算机科学 2025-05-30 Arseniy Pertzovsky , Roni Stern , Ariel Felner , Roie Zivan

In environments where many automated guided vehicles (AGVs) operate, planning efficient, collision-free paths is essential. Related research has mainly focused on environments with pre-defined passages, resulting in space inefficiency. We…

多智能体系统 · 计算机科学 2025-11-27 Hiroya Makino , Yoshihiro Ohama , Seigo Ito

Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspace modeled as a graph, A*-based approaches…

机器人学 · 计算机科学 2022-02-16 Zhongqiang Ren , Sivakumar Rathinam , Howie Choset

Avoiding collisions is the core problem in multi-agent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typically avoided in a reactive fashion, relying on local…

多智能体系统 · 计算机科学 2021-07-02 Stepan Dergachev , Konstantin Yakovlev

The task of the multi-agent pathfinding (MAPF) problem is to navigate a team of agents from their start point to the goal points. However, this setup is unsuitable in the assembly line scenario, which is periodic with a long working hour.…

多智能体系统 · 计算机科学 2025-05-15 Mingkai Tang , Lu Gan , Kaichen Zhang