1 code implementation • 8 Aug 2023 • Keisuke Okumura
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.
1 code implementation • 5 May 2023 • Keisuke Okumura
LaCAM is a sub-optimal search-based algorithm that uses lazy successor generation to dramatically reduce the planning effort.
no code implementations • 25 Nov 2022 • Keisuke Okumura, Sébastien Tixeuil
We study a novel graph path planning problem for multiple agents that may crash at runtime, and block part of the workspace.
no code implementations • 24 Nov 2022 • Keisuke Okumura
We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM).
1 code implementation • 24 Jan 2022 • Keisuke Okumura, Ryo Yonetani, Mai Nishimura, Asako Kanezaki
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance.
1 code implementation • 27 May 2020 • Keisuke Okumura, Yasumasa Tamura, Xavier Défago
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.
Multiagent Systems Robotics
1 code implementation • 24 May 2019 • Keisuke Okumura, Yasumasa Tamura, Xavier Défago
The problem of Multi-agent Path Finding (MAPF) consists in providing agents with efficient paths while preventing collisions.
Multiagent Systems Distributed, Parallel, and Cluster Computing Robotics
2 code implementations • 31 Jan 2019 • Keisuke Okumura, Manao Machida, Xavier Défago, Yasumasa Tamura
In the Multi-Agent Path Finding (MAPF) problem, a set of agents moving on a graph must reach their own respective destinations without inter-agent collisions.
Multiagent Systems Distributed, Parallel, and Cluster Computing Robotics 68W99
