Insect-scale micro-aerial vehicles, especially, lightweight, flapping-wing robots, are becoming increasingly important for safe motion sensing in spatially constrained environments such as living spaces. However, yaw control using flapping wings is fundamentally more difficult than using rotating wings. In this study, an insect-scale, tailless robot with four paired tilted flapping wings (weighing 1.52 g) to enable yaw control was fabricated. It benefits from the simplicity of a directly driven wing actuator with no transmission and a lift control signal; however, it still has an offset in the lift force. Therefore, an adaptive controller was designed to alleviate the offset. Numerical experiments confirm that the proposed controller outperforms the linear quadratic integral controller. Finally, in a tethered and controlled demonstration flight, the yaw drift was suppressed by the wing-tilting arrangement and the proposed controller. The simple structure drive system demonstrates the potential for future controlled flights of battery-powered, tailless, flapping-wing robots weighing less than 10 grams.

German Bionic just released the fifth generation Cray X AI-enhanced power suit, or exoskeleton, to help those billions of people with almost 70 pounds of additional lifting capacity, reducing the risk of back injury and repetitive stress injuries. The Cray X is already in use at BMW, IKEA, and the French delivery service DPD, and will be launched internationally in January 2022. The AI-powered suit boosts productivity, reduces error rates, decreases accidents, and results in a 25% reduction in the number of sick days workers take, German Bionic says. The smart exoskeleton market has been estimated to be growing 41.3% a year to a nearly $2 billion industry by 2025, with applications in construction, shipping and receiving, healthcare, and the military. German Bionic CEO Armin Schmidt thinks that within five years this kind of smart exoskeleton capability could help the injured, aged, and disabled to walk, run, or even play sports.

Vortex induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics (CFD) methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier-Stokes equations coupled with the structure's dynamic motion equation. In the first case, given scattered data in space-time on the velocity field and the structure's motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure's dynamic motion. In the second case, given scattered data in space-time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.