Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree

Abstract

Multi-Agent Path Finding (MAPF) and Combined Target-Assignment and Path-Finding problem (TAPF) arise in many applications such as robotics, computer gaming, warehouse automation and traffic management at road intersections.
Combined Target-Assignment and Path-Finding problem (TAPF) requires simultaneously assigning targets to agents and planning collision-free
paths for agents from their start locations to their assigned targets.
As a leading approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA) leverages both K-best target assignments to create multiple search trees and Conflict-Based Search (CBS) to resolve collisions in each search tree.
While being able to find an optimal solution, CBS-TA suffers from scalability due to the duplicated collision resolution in multiple trees and the expensive computation of K-best assignments.
We therefore develop Incremental Target Assignment Conflict-Based Search (ITA-CBS) to bypass these two computational bottlenecks.
ITA-CBS generates only a single search tree and avoids computing K-best assignments by incrementally computing new 1-best assignments during the search.
We show that, in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice, is computationally efficient.