Generalizing Multi-Agent Path Finding for Heterogeneous Agents

Abstract

Multi-Agent Path Finding (MAPF) is the problem of finding non-colliding paths for multiple agents. The classical problem assumes that all agents are homogeneous, with a fixed size and behavior. However, in reality agents are heterogeneous, with different sizes and behaviors. In this paper, we generalize MAPF to G-MAPF for the case of heterogeneous agents. We then show how two previous settings of large agents and k-robust agents are special cases of G-MAPF. Finally, we introduce G-CBS, a variant of the Conflict-Based Search (CBS) algorithm for G-MAPF, which does not cause significant extra overhead.

Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub

“…However, we wish to avoid planning from scratch when a delay is encountered. MAPF with heterogeneous agents has been proposed before (Atzmon et al 2020b) and a MAPF model was developed specifically for train routing ( Švancara and Barták 2022), which also includes the length of a train agent, but does not allow for different agent speeds. Similarly, the Flatland challenge also addressed train routing from a MAPF perspective and included delays (Mo-hanty et al 2020).…”

Prioritized Anytime Dynamic A* Path Planning for Multi-Agent Vehicles in Heterogeneous Traffic

“…In [2], the so-called UM* (Uncertainty M*) as an equivalent to A* was applied in static environments and a single-agent formulation that can deal with uncertainty in poses of all agents in the multi-agent system context. A G-CBS (Generalized Conflict-based Search) generalized MAPF for heterogeneous agents (G-MAPF) was suggested [3]. The kinematic conditions of the agents were first considered in [4] when planning in MAPF.…”