Papers Multi-Agent Path Finding
“Multi-Agent Path Finding” 태그가 달린 논문 140편 · 필터 해제
Multi-agent Path Finding for Timed Tasks using Evolutionary Games
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 …
Multi-Agent Path FindingReinforcement Learning (RL)Large-Scale Multi-Robot Coverage Path Planning on Grids with Path Deconfliction
We study Multi-Robot Coverage Path Planning (MCPP) on a 4-neighbor 2D grid G, which aims to compute paths for multiple robots to cover all cells of G. Traditional approaches are limited as they first compute coverage tre…
Multi-Agent Path FindingDeploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently, this field has embraced learning-based m…
Imitation LearningMulti-Agent Path FindingLayered LA-MAPF: a decomposition of large agent MAPF instance to accelerate solving without compromising solvability
Multi-Agent Path Finding (MAPF) has been widely studied in recent years. However, most existing MAPF algorithms assume that an agent occupies only a single grid in a grid-based map. This assumption limits their applicabi…
Multi-Agent Path FindingMFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation
We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickl…
Multi-Agent Path FindingQ-LearningWindowed MAPF with Completeness Guarantees
Traditional multi-agent path finding (MAPF) methods try to compute entire start-goal paths which are collision free. However, computing an entire path can take too long for MAPF systems where agents need to replan fast. …
Heuristic SearchMulti-Agent Path FindingMulti-agent Path Finding in Continuous Environment
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 Continu…
Multi-Agent Path FindingMulti-agent Path Finding for Mixed Autonomy Traffic Coordination
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 autonomou…
Autonomous DrivingMulti-Agent Path FindingMulti-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses
Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy sim…
Multi-Agent Path FindingAnytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the current state-of-the-art approach where a f…
Multi-Agent Path FindingSelf-LearningThompson SamplingAlgorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding
Multi-agent path finding (MAPF) is the problem of finding paths for multiple agents such that they do not collide. This problem manifests in numerous real-world applications such as controlling transportation robots in a…
Graph EmbeddingMulti-Agent Path FindingSelf-Driving CarsMGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding Problem
With the expansion of the scale of robotics applications, the multi-goal multi-agent pathfinding (MG-MAPF) problem began to gain widespread attention. This problem requires each agent to visit pre-assigned multiple goal …
Multi-Agent Path FindingScaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning a…
Future predictionMulti-Agent Path FindingITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem
Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a target to each agent also presents a challenge. The Combined Tar…
Multi-Agent Path FindingImproving Learnt Local MAPF Policies with Heuristic Search
Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically employ heuristic search to find solutio…
Heuristic SearchMulti-Agent Path FindingAccelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences
An exciting frontier in robotic manipulation is the use of multiple arms at once. However, planning concurrent motions is a challenging task using current methods. The high-dimensional composite state space renders many …
Motion PlanningMulti-Agent Path FindingRobot ManipulationA Real-Time Rescheduling Algorithm for Multi-robot Plan Execution
One area of research in multi-agent path finding is to determine how replanning can be efficiently achieved in the case of agents being delayed during execution. One option is to reschedule the passing order of agents, i…
Multi-Agent Path FindingCaching-Augmented Lifelong Multi-Agent Path Finding
Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to agents as soon as they complete their i…
Multi-Agent Path FindingRouting and Scheduling in Answer Set Programming applied to Multi-Agent Path Finding: Preliminary Report
We present alternative approaches to routing and scheduling in Answer Set Programming (ASP), and explore them in the context of Multi-agent Path Finding. The idea is to capture the flow of time in terms of partial orders…
Multi-Agent Path FindingSchedulingEnsembling Prioritized Hybrid Policies for Multi-agent Pathfinding
Multi-Agent Reinforcement Learning (MARL) based Multi-Agent Path Finding (MAPF) has recently gained attention due to its efficiency and scalability. Several MARL-MAPF methods choose to use communication to enrich the inf…
Multi-Agent Path FindingMulti-agent Reinforcement LearningQ-Learning