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" Flocks of Artificially Intelligent Swimming Micro-Robots with Long-Range Hydrodynamic Interaction and Objectives "
Mirzakhanloo, Mehdi
Alam, Mohammad-Reza
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1111980
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TLpq2511904411
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| Main Entry
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Alam, Mohammad-Reza
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Mirzakhanloo, Mehdi
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| Title & Author
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Flocks of Artificially Intelligent Swimming Micro-Robots with Long-Range Hydrodynamic Interaction and Objectives\ Mirzakhanloo, MehdiAlam, Mohammad-Reza
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| College
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University of California, Berkeley
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| Date
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2020
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| student score
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2020
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| Degree
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Ph.D.
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| Page No
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123
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| Abstract
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This dissertation addresses various aspects of realizing a three-dimensional (3D) controlled flock of swimming micro-robots that operate in, and cooperatively influence, viscous fluid environments. A systematic approach is then presented to equip the agents with an adaptive decision-making intelligence, so as to enable flocks of these artificially intelligent swimming micro-robots to achieve various objectives in the presence of flow-mediated interactions. In the first part of this dissertation, we introduce a versatile swimming robot with full 3D maneuverability in viscous environments. The experimental realization of this artificial low-Reynolds swimmer is then reported, and a hierarchical control strategy is implemented to perform various swimming maneuvers. The major challenge, which makes the swarm-control of swimming micro-robots substantially different from other well-studied swarms, is the presence of long-range flow-mediated (i.e. hydrodynamic) interactions. Therefore, the second part of this dissertation is devoted to the investigation of swarm hydrodynamics, including mutual interactions between these micro-swimmers, and their behavior in vicinity of solid boundaries. In particular, we unveil orbital topologies of interacting micro-swimmers, and report diverse families of attractors including dynamical equilibria, bound orbits, braids, and pursuit-evasion games. The third part of this dissertation is focused on optimal swarm-control strategies for swimming micro-robots to achieve various objectives in the presence of flow-mediated interactions. We show that micro-swimmers can form a concealed swarm through synergistic cooperation in suppressing one another's disturbing flows. Various control schemes are then demonstrated for the concealed swarming and stealthy maneuvers of swimming micro-robots. We also discuss how state-of-the-art reinforcement learning algorithms can be used to realize flocks of artificially intelligent swimming micro-robots. Specifically, a systematic approach is presented to equip the swimming micro-robots with an adaptive decision-making intelligence in response to non-linearly varying hydrodynamic loads. Flocks of these artificially intelligent micro-swimmers are then deployed to actively cloak swimming targets in a crowded environment. This study provides a road-map toward engineering cooperative flocks of smart micro-swimmers capable of accomplishing a new class of group-objectives. We, therefore, hope that it will spur further research on this field at the intersection of fluid mechanics, robotics and artificial intelligence.
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Mechanical engineering
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Robotics
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