Optimal strategies for the control of autonomous vehicles in data assimilation

TL;DR

This paper introduces a method to compute optimal control paths for autonomous vehicles aimed at inferring velocity fields, optimizing their trajectories based on the variance of the expected posterior distribution.

Contribution

It presents a novel control algorithm that uses vehicle direction as a control variable to minimize uncertainty in flow inference.

Findings

Effective control strategies near hyperbolic fixed points

Reduction in posterior variance through optimal paths

Applicability to linear flow models

Abstract

We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for the locally optimal control algorithm presented here, with objective formed from the variance trace of the expected posterior distribution. We present results for linear flows near hyperbolic fixed points.