Improving Continuous-time Conflict Based Search

TL;DR

This paper enhances Continuous-time Conflict-Based Search (CCBS) by integrating proven CBS improvements, significantly boosting its scalability and ability to solve larger multi-agent pathfinding problems in continuous time.

Contribution

The paper adapts key CBS improvements like conflict prioritization, disjoint splitting, and heuristics to the continuous-time setting of CCBS, addressing previous scalability limitations.

Findings

CCBS with improvements solves nearly twice as many agents.

Enhanced CCBS outperforms vanilla CCBS on various graph types.

The approach extends CBS techniques effectively to continuous-time domains.

Abstract

Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and $2^k$-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multiagent path finding in continuous-time domains.