Cargando…

Finite-horizon, energy-efficient trajectories in unsteady flows

Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constra...

Descripción completa

Detalles Bibliográficos
Autores principales: Krishna, Kartik, Song, Zhuoyuan, Brunton, Steven L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808707/
https://www.ncbi.nlm.nih.gov/pubmed/35197801
http://dx.doi.org/10.1098/rspa.2021.0255
_version_ 1784643905725136896
author Krishna, Kartik
Song, Zhuoyuan
Brunton, Steven L.
author_facet Krishna, Kartik
Song, Zhuoyuan
Brunton, Steven L.
author_sort Krishna, Kartik
collection PubMed
description Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.
format Online
Article
Text
id pubmed-8808707
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-88087072022-02-22 Finite-horizon, energy-efficient trajectories in unsteady flows Krishna, Kartik Song, Zhuoyuan Brunton, Steven L. Proc Math Phys Eng Sci Research Articles Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring. The Royal Society 2022-02 2022-02-02 /pmc/articles/PMC8808707/ /pubmed/35197801 http://dx.doi.org/10.1098/rspa.2021.0255 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Krishna, Kartik
Song, Zhuoyuan
Brunton, Steven L.
Finite-horizon, energy-efficient trajectories in unsteady flows
title Finite-horizon, energy-efficient trajectories in unsteady flows
title_full Finite-horizon, energy-efficient trajectories in unsteady flows
title_fullStr Finite-horizon, energy-efficient trajectories in unsteady flows
title_full_unstemmed Finite-horizon, energy-efficient trajectories in unsteady flows
title_short Finite-horizon, energy-efficient trajectories in unsteady flows
title_sort finite-horizon, energy-efficient trajectories in unsteady flows
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808707/
https://www.ncbi.nlm.nih.gov/pubmed/35197801
http://dx.doi.org/10.1098/rspa.2021.0255
work_keys_str_mv AT krishnakartik finitehorizonenergyefficienttrajectoriesinunsteadyflows
AT songzhuoyuan finitehorizonenergyefficienttrajectoriesinunsteadyflows
AT bruntonstevenl finitehorizonenergyefficienttrajectoriesinunsteadyflows