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Multi-Agent Path Finding

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Showing 1–50 of 140 papers

TitleStatusHype
Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields—0
Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding—0
Multi-Agent Path Finding For Large Agents Is Intractable—0
Anytime Single-Step MAPF Planning with Anytime PIBT—0
Real-Time LaCAM—0
Prioritized Planning for Continuous-time Lifelong Multi-agent Pathfinding—0
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower BoundsCode0
RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks—0
SIGMA: Sheaf-Informed Geometric Multi-Agent PathfindingCode0
A parallelizable variant of HCA*—0
Hybrid Quantum-Classical Multi-Agent Pathfinding—0
Graph Coloring to Reduce Computation Time in Prioritized PlanningCode1
Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion PlanningCode1
Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism—0
Multi-Agent Path Finding under Limited Communication Range Constraint via Dynamic Leading—0
Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models—0
Speedup Techniques for Switchable Temporal Plan Graph Optimization—0
Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State SearchCode0
Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous ActionsCode0
Transient Multi-Agent Path Finding for Lifelong Navigation in Dense Environments—0
Multi-agent Path Finding for Timed Tasks using Evolutionary Games—0
Large-Scale Multi-Robot Coverage Path Planning on Grids with Path DeconflictionCode1
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding—0
Layered LA-MAPF: a decomposition of large agent MAPF instance to accelerate solving without compromising solvabilityCode0
MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation—0
Windowed MAPF with Completeness Guarantees—0
Multi-agent Path Finding in Continuous Environment—0
Multi-agent Path Finding for Mixed Autonomy Traffic Coordination—0
Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses—0
Anytime Multi-Agent Path Finding with an Adaptive Delay-Based HeuristicCode0
Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding—0
MGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding ProblemCode0
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and OpportunitiesCode1
ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding ProblemCode1
Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences—0
Improving Learnt Local MAPF Policies with Heuristic Search—0
A Real-Time Rescheduling Algorithm for Multi-robot Plan ExecutionCode0
Caching-Augmented Lifelong Multi-Agent Path FindingCode0
Routing and Scheduling in Answer Set Programming applied to Multi-Agent Path Finding: Preliminary Report—0
Ensembling Prioritized Hybrid Policies for Multi-agent PathfindingCode2
A Conflict-Aware Optimal Goal Assignment Algorithm for Multi-Robot Systems—0
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints—0
Guidance Graph Optimization for Lifelong Multi-Agent Path FindingCode1
Multi-agent Path Finding for Cooperative Autonomous DrivingCode1
Scalable Mechanism Design for Multi-Agent Path FindingCode0
Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet—0
Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution—0
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood SearchCode1
Improved Anonymous Multi-Agent Path Finding Algorithm—0
Optimizing Crowd-Aware Multi-Agent Path Finding through Local Communication with Graph Neural Networks—0
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