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FLOW-MEDIATED NAVIGATION AND COORDINATION OF ARTIFICIAL SWIMMERS USING DEEP REINFORCEMENT LEARNING

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Date Issued:
2024
Abstract/Description:
Aquatic organisms are able to achieve swimming efficiencies that are much higher than any underwater vehicle that has been designed by humans. This is mainly due to the adaptive swimming patterns that they display in response to changes in their environment and their behaviors, i.e., hunting, fleeing, or foraging. In this work, we explore these adaptations from a hydrodynamics standpoint, using numerical simulations to emulate self-propelled artificial swimmers in various flow fields. Apart from still or uniform flow, the most likely flow field encountered by swimmers are those formed by the wakes of solid objects, such as roots of aquatic vegetation, or underwater structures. Therefore, a simplified bio-inspired design of porous structures consisting of nine cylinders was considered to identify arrangements that could produce wakes of varying velocities and enstrophy, which in turn might provide beneficial environments for underwater swimmers. These structures were analyzed using a combination of numerical simulations and experiments, and the underlying flow physics was examined using a variety of data-analysis techniques. Subsequently, in order to recreate the adaptations of natural swimmers in different flow regimes, artificial swimmers were positioned in each of these different types of flow fields and then trained to optimize their movements to maximize swimming efficiency using deep reinforcement learning. These artificial swimmers utilize a sensory input system that allows them to detect the velocity field and pressure on the surface of their body, which is similar to the lateral line sensing system in biological fish. The results demonstrate that the information gleaned from the simplified lateral line system was sufficient for the swimmer to replicate naturally found behaviors such as K´arm´an gaiting. The phenomenon of schooling in underwater organisms is similarly thought to provide opportunities for swimmers to increase their energy efficiency, along with the other associated benefits. Thus, multiple swimmers were trained using multi-agent reinforcement learning to discover optimal swimming patterns at the group level as well as the individual level.
Title: FLOW-MEDIATED NAVIGATION AND COORDINATION OF ARTIFICIAL SWIMMERS USING DEEP REINFORCEMENT LEARNING.
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Name(s): Nair, Aishwarya, author
Verma, Siddhartha , Thesis advisor
Florida Atlantic University, Degree grantor
Department of Ocean and Mechanical Engineering
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2024
Date Issued: 2024
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 189 p.
Language(s): English
Abstract/Description: Aquatic organisms are able to achieve swimming efficiencies that are much higher than any underwater vehicle that has been designed by humans. This is mainly due to the adaptive swimming patterns that they display in response to changes in their environment and their behaviors, i.e., hunting, fleeing, or foraging. In this work, we explore these adaptations from a hydrodynamics standpoint, using numerical simulations to emulate self-propelled artificial swimmers in various flow fields. Apart from still or uniform flow, the most likely flow field encountered by swimmers are those formed by the wakes of solid objects, such as roots of aquatic vegetation, or underwater structures. Therefore, a simplified bio-inspired design of porous structures consisting of nine cylinders was considered to identify arrangements that could produce wakes of varying velocities and enstrophy, which in turn might provide beneficial environments for underwater swimmers. These structures were analyzed using a combination of numerical simulations and experiments, and the underlying flow physics was examined using a variety of data-analysis techniques. Subsequently, in order to recreate the adaptations of natural swimmers in different flow regimes, artificial swimmers were positioned in each of these different types of flow fields and then trained to optimize their movements to maximize swimming efficiency using deep reinforcement learning. These artificial swimmers utilize a sensory input system that allows them to detect the velocity field and pressure on the surface of their body, which is similar to the lateral line sensing system in biological fish. The results demonstrate that the information gleaned from the simplified lateral line system was sufficient for the swimmer to replicate naturally found behaviors such as K´arm´an gaiting. The phenomenon of schooling in underwater organisms is similarly thought to provide opportunities for swimmers to increase their energy efficiency, along with the other associated benefits. Thus, multiple swimmers were trained using multi-agent reinforcement learning to discover optimal swimming patterns at the group level as well as the individual level.
Identifier: FA00014413 (IID)
Degree granted: Dissertation (PhD)--Florida Atlantic University, 2024.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Reinforcement learning
Hydrodynamics
Computational fluid dynamics
.
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014413
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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Host Institution: FAU