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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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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 |
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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 |
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