A decomposition approach to multi-vehicle cooperative control

  • Authors:

  • Matthew G. Earl;Raffaello D'Andrea

  • Affiliations:

  • BAE Systems, Advanced Information Technologies, Burlington, MA, United States;Department Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, United States

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2007

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Abstract

We use a decomposition approach to generate cooperative strategies for a class of multi-vehicle control problems. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decompose 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 utilize our algorithms in a hierarchical model predictive control architecture with a variable replanning rate at each level to provide feedback in dynamically changing and uncertain environments.