Information and Multi-Sensor Coordination

TL;DR

This paper explores the coordination and control of multi-sensor systems using team decision theory, addressing the challenges of integrating uncertain, partial, and disparate sensor information for robust decision-making.

Contribution

It introduces a novel approach applying team decision theory to multi-sensor coordination, including new analytic results and simulation analysis of sensor aggregation under uncertainty.

Findings

Analytic results on multi-sensor data aggregation

Simulation demonstrates robust sensor cooperation

Insights applicable to multi-robot systems and AI decision-making

Abstract

The control and integration of distributed, multi-sensor perceptual systems is a complex and challenging problem. The observations or opinions of different sensors are often disparate incomparable and are usually only partial views. Sensor information is inherently uncertain and in addition the individual sensors may themselves be in error with respect to the system as a whole. The successful operation of a multi-sensor system must account for this uncertainty and provide for the aggregation of disparate information in an intelligent and robust manner. We consider the sensors of a multi-sensor system to be members or agents of a team, able to offer opinions and bargain in group decisions. We will analyze the coordination and control of this structure using a theory of team decision-making. We present some new analytic results on multi-sensor aggregation and detail a simulation which we use to investigate our ideas. This simulation provides a basis for the analysis of complex agent structures cooperating in the presence of uncertainty. The results of this study are discussed with reference to multi-sensor robot systems, distributed Al and decision making under uncertainty.