activity
20192023
most citedImproving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

5 citations · 14 across the 6 of their papers we have counts for

collaborators

8 papers

cs.AI2023

Engineering LaCAM: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding

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. LaCAM…

cs.AI20235 cited

Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

Keisuke Okumura

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 d…

cs.RO2022

Fault-Tolerant Offline Multi-Agent Path Planning

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. In our setting, agents can detect neighboring crashed a…

cs.AI20224 cited

LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding

Keisuke Okumura

We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of finding collision-free paths…

cs.MA20224 cited

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces

Keisuke Okumura, Ryo Yonetani, Mai Nishimura +1

Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance. One promising approach is to first construct graphs approximat…

cs.RO20211 cited

Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving

Mai Hirata, Manabu Tsukada, Keisuke Okumura +3

Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS app…