Physics-informed reinforcement learning for motion control of a fish-like swimming robot.

Motion control of fish-like swimming robots presents many challenges due to the unstructured environment and unmodelled governing physics of the fluid-robot interaction. Commonly used low-fidelity control models using simplified formulas for drag and lift forces do not capture key physics that can play an important role in the dynamics of small-sized robots with limited actuation. Deep Reinforcement Learning (DRL) holds considerable promise for motion control of robots with complex dynamics.

Proprioceptive wake classification by a body with a passive tail.

The remarkable ability of some marine animals to identify flow structures and parameters using complex non-visual sensors, such as lateral lines of fish and the whiskers of seals, has been an area of investigation for researchers looking to apply this ability to artificial robotic swimmers, which could lead to improvements in autonomous navigation and efficiency. Several species of fish in particular have been known to school effectively, even when blind. Beyond specialized sensors like the lateral lines, it is now known that some fish use purely proprioceptive sensing, using the kinematics of their fins or tails to sense their surroundings.

Learning hydrodynamic signatures through proprioceptive sensing by bioinspired swimmers.

Objects moving in water or stationary objects in streams create a vortex wake. Such vortex wakes encode information about the objects and the flow conditions. Underwater robots that often function with constrained sensing capabilities can benefit from extracting this information from vortex wakes.

Chaotic advection and mixing by a pair of microrotors in a circular domain.

In this work we study chaotic mixing induced by point microrotors in a bounded two-dimensional Stokes flow. The dynamics of the pair of rotors, modeled as rotlets, are non-Hamiltonian in the bounded domain and produce chaotic advection of fluid tracers in subsets of the domain. A complete parametric investigation of the fluid mixing as a function of the initial locations of the rotlets is performed based on pseudophase portraits.

Dynamics of groups of magnetically driven artificial microswimmers.

Magnetically driven artificial microswimmers have the potential to revolutionize many biomedical technologies, such as minimally invasive microsurgery, microparticle manipulation, and localized drug delivery. However, many of these applications will require the controlled dynamics of teams of these microrobots with minimal feedback. In this work, we study the motion and fluid dynamics produced by groups of artificial microswimmers driven by a torque induced through a uniform, rotating magnetic field.

Particle slip velocity influences inertial focusing of particles in curved microchannels.

Size based separation and identification of particles in microfluidics through purely hydrodynamic means has gained significant interest due to a number of possible biomedical applications. Curved micro-channels, particularly spiral micro-channels with rectangular cross-section and the dynamics of particles in such channels have been extensively researched to achieve size based separation of particles. In this paper we present evidence that sheds new light on the dynamics of particles in such curved channels; that a unique particle slip velocity is associated with the focusing positions in the cross sections, which leads to a balance of forces.

Particle segregation by Stokes number for small neutrally buoyant spheres in a fluid.

It is a commonly observed phenomenon that spherical particles with inertia in an incompressible fluid do not behave as ideal tracers. Due to the inertia of the particle, the planar dynamics are described in a four-dimensional phase space and thus can differ considerably from the ideal tracer dynamics. Using finite-time Lyapunov exponents, we compute the sensitivity of the final position of a particle with respect to its initial velocity, relative to the fluid, and thus partition the relative velocity subspace at each point in configuration space.