Scalable agent distribution matching using partitioned optimal transport
Fast and Scalable Multi-Agent Distribution Matching via Partitioned Optimal Transport
Multiagent SystemsRobotics
Summary
Setting up a group of agents to match a target arrangement can be slow when considering everyone at once. The authors show a way to split the problem into smaller parts handled locally, which speeds things up while still working well overall. This method works for agents moving in different ways and keeps improving over time. They tested their approach with simulations to show it really does what it promises.
What this means in practice
- •For robotics developers: Coordinate a large group of robots to reach a desired spatial configuration efficiently by solving local smaller problems instead of one big problem.
- •For drone fleet operators: Assign drones to target locations in complex environments with scalable computations for real-time distribution matching and control.
Authors
Kooktae Lee, Ruchika Singh
Abstract
This paper presents a scalable optimal-transport-based framework for terminal distribution matching in multi-agent systems. While optimal transport provides a natural way to measure distributional mismatch and assign agents to a desired spatial distribution, global discrete transport can become computationally expensive for large-scale systems. We address this bottleneck by partitioning agents and target samples into spatially corresponding blocks and solving smaller local transport problems. Under a mass-balance condition, the resulting restricted coupling remains feasible for the global problem and provides an upper bound on the Wasserstein cost. The local assignments generate target locations for finite-horizon agent control, applicable to both linear and nonlinear dynamics. By alternating local assignment and control, we establish a cycle-to-cycle descent guarantee for the resulting transport surrogate. The proposed framework therefore enables scalable terminal distribution matching while retaining a rigorous connection to the Wasserstein objective. The technical soundness of the proposed results is validated through simulations.