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相关论文: MAPP: a Scalable Multi-Agent Path Planning Algorit…

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Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This problem is computationally complex, especially when dealing…

This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the…

人工智能 · 计算机科学 2024-01-23 Keisuke Okumura

Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally efficient approach…

多智能体系统 · 计算机科学 2025-01-22 Patrick Scheffe , Julius Kahle , Bassam Alrifaee

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF was on grids, assumed agents' actions have uniform duration,…

人工智能 · 计算机科学 2019-06-17 Anton Andreychuk , Konstantin Yakovlev , Dor Atzmon , Roni Stern

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

Multi-agent pathfinding (MAPF) holds significant utility within autonomous systems, however, the calculation and memory space required for multi-agent path finding (MAPF) grows exponentially as the number of agents increases. This often…

机器人学 · 计算机科学 2025-03-11 Zhuo Yao , Wei Wang

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents given individual start and goal states. Given the hardness of MAMP, most of the research related to multi-agent systems has focused on…

机器人学 · 计算机科学 2020-03-05 Irving Solis , Read Sandström , James Motes , Nancy M. Amato

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

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized…

人工智能 · 计算机科学 2017-07-21 Anton Andreychuk , Konstantin Yakovlev

In the Multiagent Path Finding problem (MAPF for short), we focus on efficiently finding non-colliding paths for a set of $k$ agents on a given graph $G$, where each agent seeks a path from its source vertex to a target. An important…

计算复杂性 · 计算机科学 2023-12-18 Foivos Fioravantes , Dušan Knop , Jan Matyáš Křišťan , Nikolaos Melissinos , Michal Opler

Multi Agent Path Finding (MAPF) requires identification of conflict free paths for agents which could be point-sized or with dimensions. In this paper, we propose an approach for MAPF for spatially-extended agents. These find application in…

多智能体系统 · 计算机科学 2021-06-10 Shyni Thomas , M. Narasimha Murty

Multi-Agent Path Finding (MAPF) is a fundamental motion coordination problem arising in multi-agent systems with a wide range of applications. The problem's intractability has led to extensive research on improving the scalability of…

多智能体系统 · 计算机科学 2023-05-26 Tzvika Geft

This study extends the recently-developed LaCAM algorithm for multi-agent pathfinding (MAPF). LaCAM is a sub-optimal search-based algorithm that uses lazy successor generation to dramatically reduce the planning effort. We present two…

人工智能 · 计算机科学 2023-05-08 Keisuke Okumura

In this work we consider the multi-agent motion planning (MAMP) problem with the constraint that agents arrive at their respective goals at the same time. For the special case where all agents are initially at rest we propose a two-step…

最优化与控制 · 数学 2026-05-05 Anja Hellander , Daniel Axehill

Multi-agent path finding (MAPF) is the problem of moving agents to the goal vertex without collision. In the online MAPF problem, new agents may be added to the environment at any time, and the current agents have no information about…

多智能体系统 · 计算机科学 2023-01-12 Mingkai Tang , Boyi Liu , Yuanhang Li , Hongji Liu , Ming Liu , Lujia Wang

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

Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence in which one needs to find a set of collision-free paths for a group of mobile agents (robots) operating in the shared workspace. Due to its…

机器人学 · 计算机科学 2021-08-12 Zain Alabedeen Ali , Konstantin Yakovlev

In multi-agent applications such as surveillance and logistics, fleets of mobile agents are often expected to coordinate and safely visit a large number of goal locations as efficiently as possible. The multi-agent planning problem in these…

机器人学 · 计算机科学 2021-11-09 Zhongqiang Ren , Sivakumar Rathinam , Howie Choset

Multi-Agent Path Finding (MAPF) seeks collision-free paths for multiple agents from their respective starting locations to their respective goal locations while minimizing path costs. Although many MAPF algorithms were developed and can…

多智能体系统 · 计算机科学 2024-12-24 Shuai Zhou , Shizhe Zhao , Zhongqiang Ren

Multi-Agent Path Finding (MAPF) requires collision-free trajectories for multiple agents on a shared graph, often with the objective of minimizing the sum-of-costs (SOC). Many optimal and bounded-suboptimal solvers rely on time-expanded…

多智能体系统 · 计算机科学 2026-04-08 Fernando Salanova , Eduardo Montijano , Cristian Mahulea
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