A plethora of active matter models exist that describe the behavior of self-propelled particles (or swimmers), both with and without hydrodynamics. However, there are few studies that consider shape-anisotropic swimmers and include hydrodynamic interactions. Here, we introduce a simple method to simulate self-propelled colloids interacting hydrodynamically in a viscous medium using the lattice-Boltzmann technique. Our model is based on raspberry-type viscous coupling and a force/counter-force formalism, which ensures that the system is force free. We consider several anisotropic shapes and characterize their hydrodynamic multipolar flow field. We demonstrate that shape-anisotropy can lead to the presence of a strong quadrupole and octupole moments, in addition to the principle dipole moment. The ability to simulate and characterize these higher-order moments will prove crucial for understanding the behavior of model swimmers in confining geometries.

Hydrodynamic interactions beyond the principal dipole are crucial to understand the formation of the oscillatory trajectories of microswimmers under confinement.

The comparatively poor positioning accuracy of industrial robots limits or even prevents their use in many industrial applications. In contrast to other fields of robotic research, robot accuracy improvement has not been significantly boosted by machine learning-based methods yet. For this reason, we carried out four comprehensive series of measurements using a high-precision laser tracker together with a widely used 6-axis articulated robot. The data will be made publicly available to serve as benchmark data for different techniques. Along with the dataset, this paper introduces a hybrid neural network-based approach to compensate both geometric and non-geometric error sources and increase robot positioning accuracy. We compare our method to previous works and demonstrate advanced results.