A Decomposition Approach to Multi-Vehicle Cooperative Control

TL;DR

This paper introduces a decomposition-based method for multi-vehicle cooperative control, combining task assignment and execution to enable real-time, near-optimal strategies in adversarial scenarios.

Contribution

It presents a novel decomposition approach that separates task assignment from execution, along with a real-time solver and multi-level architecture for dynamic environments.

Findings

Near-optimal task assignment solver demonstrated in real-time

Phase transitions observed in adversarial game scenarios

Multi-level architecture with variable replanning rates implemented

Abstract

We present methods that generate cooperative strategies for multi-vehicle control problems using a decomposition approach. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decomposed the problem into a combinatorial component and a continuous component. The continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. In this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. To motivate our methods, we apply them to an adversarial game between two teams of vehicles. One team is governed by simple rules and the other by our algorithms. In our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. Finally, we implement our algorithms in a multi-level architecture with a variable replanning rate at each level to provide feedback on a dynamically changing and uncertain environment.