Multi-Agent Path Finding with Capacity Constraints
This work addresses path planning for multiple agents in constrained environments, but it is incremental as it extends prior SAT-based methods with capacity modifications.
The paper tackles the problem of multi-agent path finding (MAPF) by extending it to allow more than one agent per vertex, addressing capacity constraints. It studies SAT-based models for this extension and evaluates their performance, building on existing formulations like MDD-SAT and SMT-CBS.
In multi-agent path finding (MAPF) the task is to navigate agents from their starting positions to given individual goals. The problem takes place in an undirected graph whose vertices represent positions and edges define the topology. Agents can move to neighbor vertices across edges. In the standard MAPF, space occupation by agents is modeled by a capacity constraint that permits at most one agent per vertex. We suggest an extension of MAPF in this paper that permits more than one agent per vertex. Propositional satisfiability (SAT) models for these extensions of MAPF are studied. We focus on modeling capacity constraints in SAT-based formulations of MAPF and evaluation of performance of these models. We extend two existing SAT-based formulations with vertex capacity constraints: MDD-SAT and SMT-CBS where the former is an approach that builds the model in an eager way while the latter relies on lazy construction of the model.