CBS with Continuous-Time Revisit
🤖 AI Summary
This work addresses the incompleteness of Conflict-Based Continuous-time Search (CCBS) for the Continuous-time Multi-Agent Path Finding (MAPF-R) problem. Through formal modeling and satisfiability analysis, we rigorously prove— for the first time—that CCBS cannot guarantee finite termination or optimality; its fundamental incompleteness stems from a restricted search node space that violates completeness requirements. We further demonstrate the infeasibility of common repair strategies. Building upon the Conflict-Based Search (CBS) framework, we integrate continuous-time motion modeling with algorithmic complexity analysis to establish the theoretical incompleteness of CCBS in MAPF-R as a core result. This finding provides a critical theoretical foundation and principled direction for designing truly complete continuous-time multi-agent planning algorithms.
Application Category
📝 Abstract
In recent years, researchers introduced the Multi-Agent Path Finding in Continuous Time (MAPFR) problem. Conflict-based search with Continuous Time (CCBS), a variant of CBS for discrete MAPF, aims to solve MAPFR with completeness and optimality guarantees. However, CCBS overlooked the fact that search algorithms only guarantee termination and return the optimal solution with a finite amount of search nodes. In this paper, we show that CCBS is incomplete, reveal the gaps in the existing implementation, demonstrate that patching is non-trivial, and discuss the next steps.
Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Path Finding
Continuous Time Search
Optimal Solution Guarantee
Innovation
Methods, ideas, or system contributions that make the work stand out.
Continuous-Time Multi-Agent Path Finding
Conflict-Based Search
Optimization Direction
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