Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Pathfinding
non-unit edge costs
finite state space
graph discretization
collision avoidance
Multi-Agent Pathfinding with Non-Unit Integer Edge Costs via Enhanced Conflict-Based Search and Graph Discretization
🤖 AI Summary
This work addresses the challenge of multi-agent path finding in real-world scenarios where edges incur arbitrary non-unit integer costs, a setting inadequately handled by conventional approaches that assume unit costs and single-timestep actions. To bridge this gap, the authors introduce the MAPFZ problem formulation, which accommodates general non-unit integer edge costs while preserving a finite state space. The key contributions include a novel time-interval-based conflict detection mechanism, an enhanced Safe Interval Path Planning (SIPP) algorithm, an improved conflict-based search framework termed CBS-NIC, and a Bayesian optimization-based graph discretization method (BOGD) with sublinear regret bounds. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art algorithms across multiple benchmark domains, achieving notable improvements in both success rate and computational efficiency.
📝 Abstract
Multi-Agent Pathfinding (MAPF) plays a critical role in various domains. Traditional MAPF methods typically assume unit edge costs and single-timestep actions, which limit their applicability to real-world scenarios. MAPFR extends MAPF to handle non-unit costs with real-valued edge costs and continuous-time actions, but its geometric collision model leads to an unbounded state space that compromises solver efficiency. In this paper, we propose MAPFZ, a novel MAPF variant on graphs with non-unit integer costs that preserves a finite state space while offering improved realism over classical MAPF. To solve MAPFZ efficiently, we develop CBS-NIC, an enhanced Conflict-Based Search framework incorporating time-interval-based conflict detection and an improved Safe Interval Path Planning (SIPP) algorithm. Additionally, we propose Bayesian Optimization for Graph Design (BOGD), a discretization method for non-unit edge costs that balances efficiency and accuracy with a sub-linear regret bound. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in runtime and success rate across diverse benchmark scenarios.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Agent Pathfinding
Non-Unit Integer Edge Costs
Conflict-Based Search
Graph Discretization
Safe Interval Path Planning
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