Zobrazeno 1 - 10
of 29 387
pro vyhledávání: '"path finding"'
Autor:
Zang, Hongzhi, Zhang, Yulun, Jiang, He, Chen, Zhe, Harabor, Daniel, Stuckey, Peter J., Li, Jiaoyang
We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agent Path Finding (MAPF) focuses on moving multiple agents from their sta
Externí odkaz:
http://arxiv.org/abs/2411.16506
Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configuration. The Lifelong MAPF (LMAPF) problem is a well-studied online version of MAPF in which an agent rec
Externí odkaz:
http://arxiv.org/abs/2412.04256
Autor:
Jiang, He, Wang, Yutong, Veerapaneni, Rishi, Duhan, Tanishq, Sartoretti, Guillaume, Li, Jiaoyang
Lifelong Multi-Agent Path Finding (LMAPF) is a variant of MAPF where agents are continually assigned new goals, necessitating frequent re-planning to accommodate these dynamic changes. Recently, this field has embraced learning-based methods, which r
Externí odkaz:
http://arxiv.org/abs/2410.21415
Among sub-optimal Multi-Agent Path Finding (MAPF) solvers, rule-based algorithms are particularly appealing since they are complete. Even in crowded scenarios, they allow finding a feasible solution that brings each agent to its target, preventing de
Externí odkaz:
http://arxiv.org/abs/2410.07954
Autonomous multi-agent systems such as hospital robots and package delivery drones often operate in highly uncertain environments and are expected to achieve complex temporal task objectives while ensuring safety. While learning-based methods such as
Externí odkaz:
http://arxiv.org/abs/2411.10558
Scientists often search for phenomena of interest while exploring new environments. Autonomous vehicles are deployed to explore such areas where human-operated vehicles would be costly or dangerous. Online control of autonomous vehicles for informati
Externí odkaz:
http://arxiv.org/abs/2409.13065
Autor:
Janovská, Kristýna, Surynek, Pavel
We address a variant of multi-agent path finding in continuous environment (CE-MAPF), where agents move along sets of smooth curves. Collisions between agents are resolved via avoidance in the space domain. A new Continuous Environment Conflict-Based
Externí odkaz:
http://arxiv.org/abs/2409.10680
Autor:
Qian, Cheng, Zhang, Yulun, Bhatt, Varun, Fontaine, Matthew Christopher, Nikolaidis, Stefanos, Li, Jiaoyang
We use the Quality Diversity (QD) algorithm with Neural Cellular Automata (NCA) to generate benchmark maps for Multi-Agent Path Finding (MAPF) algorithms. Previously, MAPF algorithms are tested using fixed, human-designed benchmark maps. However, suc
Externí odkaz:
http://arxiv.org/abs/2409.06888
Autor:
Fekete, Sándor P., Kosfeld, Ramin, Kramer, Peter, Neutzner, Jonas, Rieck, Christian, Scheffer, Christian
We study Multi-Agent Path Finding for arrangements of labeled agents in the interior of a simply connected domain: Given a unique start and target position for each agent, the goal is to find a sequence of parallel, collision-free agent motions that
Externí odkaz:
http://arxiv.org/abs/2409.06486
In the evolving landscape of urban mobility, the prospective integration of Connected and Automated Vehicles (CAVs) with Human-Driven Vehicles (HDVs) presents a complex array of challenges and opportunities for autonomous driving systems. While recen
Externí odkaz:
http://arxiv.org/abs/2409.03881