144 citations · 199 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
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…
Quick Multi-Robot Motion Planning by Combining Sampling and Search
Keisuke Okumura, Xavier Défago
We propose a novel algorithm to solve multi-robot motion planning (MRMP) rapidly, called Simultaneous Sampling-and-Search Planning (SSSP). Conventional MRMP studies mostly take the…
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…
Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution
Keisuke Okumura, Xavier Défago
Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of Multi-Agent Path Finding (MAPF), is cru…
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…
Offline Time-Independent Multi-Agent Path Planning
Keisuke Okumura, François Bonnet, Yasumasa Tamura +1
This paper studies a novel planning problem for multiple agents that cannot share holding resources, named OTIMAPP (Offline Time-Independent Multi-Agent Path Planning). Given a gra…
Active Modular Environment for Robot Navigation
Shota Kameyama, Keisuke Okumura, Yasumasa Tamura +1
This paper presents a novel robot-environment interaction in navigation tasks such that robots have neither a representation of their working space nor planning function, instead,…
Iterative Refinement for Real-Time Multi-Robot Path Planning
Keisuke Okumura, Yasumasa Tamura, Xavier Defago
We study the iterative refinement of path planning for multiple robots, known as multi-agent pathfinding (MAPF). Given a graph, agents, their initial locations, and destinations, a…
Time-Independent Planning for Multiple Moving Agents
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 ex…
winPIBT: Extended Prioritized Algorithm for Iterative Multi-agent Path Finding
Keisuke Okumura, Yasumasa Tamura, Xavier Défago
The problem of Multi-agent Path Finding (MAPF) consists in providing agents with efficient paths while preventing collisions. Numerous solvers have been developed so far since MAPF…
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding
Keisuke Okumura, Manao Machida, Xavier Défago +1
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. In practical MAPF app…