Collective Motion, Sensor Networks, and Ocean Sampling

TLDR

The work is motivated by adaptive ocean sampling for autonomous ocean observing and prediction. The paper designs mobile sensor networks for optimal data collection. The authors use a performance metric that minimizes model‑estimate error to derive optimal sensor paths, and present feedback control laws that stably coordinate sensors on optimized structured tracks. The authors compute optimal closed‑loop solutions in low‑dimensional cases and explore robustness to steady flow fields affecting slow mobile sensors.

Abstract

This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored