A Framework for Cooperative Control Applied to a Distributed Sensor Network

This paper proposes and evaluates a framework for cooperative control of a multi-agent system. The framework is evaluated on a target-tracking application where a distributed sensor network is tasked to autonomously observe targets within the environment. The problem of cooperative control is defined using two distinct levels of cooperation: implicit and explicit. Implicit cooperation is defined as cooperation through only the exchange of environmental data to compile a common picture over which to reason locally. For example, in this paper, decentralized data fusion algorithms are used to build and update a common picture of the target positions and velocities. Explicit cooperation, which is the main focus of this paper, negotiates the agents explicitly on a joint set of actions to perform. In this paper, the problem of explicit cooperation is formulated as a distributed optimization, and a framework to find the joint set of actions is proposed. The framework utilizes two algorithms, the Max-Sum algorithm, to globally solve a factorizable utility function, and Probability Collectives (PC), to solve the individual factors of the utility function. The paper presents experimental results of the two algorithms using a simulated distributed sensor network when the tracking problem is and is not factorizable. The results show that the proposed framework can efficiently and effectively enable cooperation in a distributed sensor network. The Max-Sum algorithm provides a distributed and flexible approach to solve a factorizable utility function, where the PC algorithm was shown to efficiently solve the individual factors when more than four sensors are required to cooperate.