Frequency-dependent higher-order Stokes singularities near a planar elastic boundary: implications for the hydrodynamics of an active microswimmer near an elastic interface

The emerging field of self-driven active particles in fluid environments has recently created significant interest in the biophysics and bioengineering communities owing to their promising future biomedical and technological applications. These microswimmers move autonomously through aqueous media where under realistic situations they encounter a plethora of external stimuli and confining surfaces with peculiar elastic properties. Based on a far-field hydrodynamic model, we present an analytical theory to describe the physical interaction and hydrodynamic couplings between a self-propelled active microswimmer and an elastic interface that features resistance toward shear and bending. We model the active agent as a superposition of higher-order Stokes singularities and elucidate the associated translational and rotational velocities induced by the nearby elastic boundary. Our results show that the velocities can be decomposed in shear and bending related contributions which approach the velocities of active agents close to a no-slip rigid wall in the steady limit. The transient dynamics predict that contributions to the velocities of the microswimmer due to bending resistance are generally more pronounced than to shear resistance. Our results provide insight into the control and guidance of artificial and synthetic self-propelling active microswimmers near elastic confinements.Comment: 20 pages, 3 figures. To appear in PRE. Abstract shortened to comply with the arXiv limit of 1920 character

Flocks of Artificially Intelligent Swimming micro-Robots with Long-range Hydrodynamic Interaction and Objectives

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

Flocks of Artificially Intelligent Swimming micro-Robots with Long-range Hydrodynamic Interaction and Objectives

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

Hydrodynamic Choreographies of Microswimmers

Abstract We unveil orbital topologies of two nearby swimming microorganisms using an artificial microswimmer, called Quadroar. Depending on the initial conditions of the microswimmers, we find diverse families of attractors including dynamical equilibria, bound orbits, braids, and pursuit–evasion games. We also observe a hydrodynamic slingshot effect: a system of two hydrodynamically interacting swimmers moving along braids can advance in space faster than non-interacting swimmers that have the same actuation parameters and initial conditions as the interacting ones. Our findings suggest the existence of complex collective behaviors of microswimmers, from equilibrium to rapidly streaming states