A bioinspired Separated Flow wing provides turbulence resilience and aerodynamic efficiency for miniature drones

Shoulder viscoelasticity in a raptor-inspired model alleviates instability and enhances passive gust rejection

Recent experiments with gliding raptors reveal a perplexing dichotomy: remarkably resilient gust rejection, but, at the same time, an exceptionally high degree of longitudinal instability. To resolve this incompatibility, a multiple degree of freedom model is developed with minimal requisite complexity to examine the hypothesis that the bird shoulder joint may embed essential stabilizing and preflexive mechanisms for rejecting rapid perturbations while simplifying and reducing control effort. Thus, the formulation herein is centrally premised upon distinct wing pitch and body pitch angles coupled via a Kelvin-Voigt viscoelastic shoulder joint. The model accurately exhibits empirical gust response of an unstable gliding raptor, generates biologically plausible equilibrium configurations, and the viscoelastic shoulder coupling is shown to drastically alleviate the high degree of instability predicted by conventional linear flight dynamics models. In fact, stability analysis of the model predicts a critical system timescale (the time to double amplitude of a pitch divergence mode) that is commensurate within vivomeasured latency of barn owls (Tyto alba). Active gust mitigation is studied by presupposing the owl behaves as an optimal controller. The system is under-actuated and the feedback control law is resolved in the controllable subspace using a Kalman decomposition. Importantly, control-theoretic analysis precisely identifies what discrete gust frequencies may be rapidly and passively rejected versus disturbances requiring feedback control intervention.

Bio-inspired, sensitivity-enhanced, bi-directional airflow sensor for turbulence detection

Detecting airflow turbulence precursors promptly is crucial for ensuring flight safety and control. The initial stages of turbulence involve small reverse flows with random velocities and directions, which are not easily detected by existing airflow sensors. In this study, we designed a bionic, sensitivity-enhanced, bi-directional airflow sensor (BSBA) by incorporating bio-inspired circular tip slits and enlarging the central part of the cruciform beam structure. The BSBA exhibits a rapid response time (24.1 ms), high sensitivity (1.36 mV m-1 s-1), consistent detection of forward and backward airflow (correlation coefficient of 0.9854), and a low airflow detection threshold (1 ml). With these features, the proposed sensor can rapidly and accurately measure slight variations in the oscillating airflow, flow field, and contact force. The BSBA also achieves transparent obstacle detection on a quadrotor, even in visually challenging environments, by capturing minute changes in the flow fields produced by the quadrotor when encountering obstacles. The sensor's high sensitivity, consistent bi-directional detection, and fast response give it significant potential for enhancing safety in aircraft control systems.

Grasping extreme aerodynamics on a low-dimensional manifold

Modern air vehicles perform a wide range of operations, including transportation, defense, surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations in gusty environments, encountered in urban canyons, over mountainous terrains, and in ship wakes. With extreme weather becoming ever more frequent due to global warming, it is anticipated that aircraft, especially those that are smaller in size, will encounter sizeable atmospheric disturbances and still be expected to achieve stable flight. However, there exists virtually no theoretical fluid-dynamic foundation to describe the influence of extreme vortical gusts on wings. To compound this difficulty, there is a large parameter space for gust-wing interactions. While such interactions are seemingly complex and different for each combination of gust parameters, we show that the fundamental physics behind extreme aerodynamics is far simpler and lower-rank than traditionally expected. We reveal that the nonlinear vortical flow field over time and parameter space can be compressed to only three variables with a lift-augmented autoencoder while holding the essence of the original high-dimensional physics. Extreme aerodynamic flows can be compressed through machine learning into a low-dimensional manifold, which can enable real-time sparse reconstruction, dynamical modeling, and control of extremely unsteady gusty flows. The present findings offer support for the stable flight of next-generation small air vehicles in atmosphere conditions traditionally considered unflyable.

Flying State Sensing and Estimation Method of Large-Scale Bionic Flapping Wing Flying Robot

A large bionic flapping wing robot has unique advantages in flight efficiency. However, the fluctuation of fuselage centroid during flight makes it difficult for traditional state sensing and estimation methods to provide stable and accurate data. In order to provide stable and accurate positioning and attitude information for a flapping wing robot, this paper proposes a flight state sensing and estimation method integrating multiple sensors. Combined with the motion characteristics of a large flapping wing robot, the autonomous flight, including the whole process of takeoff, cruise and landing, is realized. An explicit complementary filtering algorithm is designed to fuse the data of inertial sensor and magnetometer, which solves the problem of attitude divergence. The Kalman filter algorithm is designed to estimate the spatial position and speed of a flapping wing robot by integrating inertial navigation with GPS (global positioning system) and barometer measurement data. The state sensing and estimation accuracy of the flapping wing robot are improved. Finally, the flying state sensing and estimation method is integrated with the flapping wing robot, and the flight experiments are carried out. The results verify the effectiveness of the proposed method, which can provide a guarantee for the flapping wing robot to achieve autonomous flight beyond the visual range.

Micro aerial vehicle with basic risk of operation

We draw current efforts towards proposing a wing-type micro UAV with characteristics of being a basic operation risk self handled (Micro-Brosh) platform. Its micro-sized wingspan and weight, which are less than 0.30 m and 0.150 kg, respectively, guarantee the low risk to the operator and installations in case of crashing. It can be launched manually without using an appropriate runway, besides using a soft grass field for landing is recommended. Its associated costs for construction and maintenance are very low (below US$ 500) if compared to traditional aircraft. The main contribution here is the architectural design, besides we provide detailed documentation including techniques for determining lift, thrust, drag, minimum flight velocity, maximum time of flight and distance (autonomy), and other issues that we have solved related to the aircraft development. We validate our prototype with experiments including several tests done with the UAV with results that have proven its flight ability. To the end, we understand that this work provides a nice starting document to researchers that intend to develop a UAV or enter this field.

Mechanosensory Hairs and Hair-like Structures in the Animal Kingdom: Specializations and Shared Functions Serve to Inspire Technology Applications

Biological mechanosensation has been a source of inspiration for advancements in artificial sensory systems. Animals rely on sensory feedback to guide and adapt their behaviors and are equipped with a wide variety of sensors that carry stimulus information from the environment. Hair and hair-like sensors have evolved to support survival behaviors in different ecological niches. Here, we review the diversity of biological hair and hair-like sensors across the animal kingdom and their roles in behaviors, such as locomotion, exploration, navigation, and feeding, which point to shared functional properties of hair and hair-like structures among invertebrates and vertebrates. By reviewing research on the role of biological hair and hair-like sensors in diverse species, we aim to highlight biological sensors that could inspire the engineering community and contribute to the advancement of mechanosensing in artificial systems, such as robotics.

Aerodynamic efficiency of gliding birds vs comparable UAVs: a review

Here, we reviewed published aerodynamic efficiencies of gliding birds and similar sized unmanned aerial vehicles (UAVs) motivated by a fundamental question: are gliding birds more efficient than comparable UAVs? Despite a multitude of studies that have quantified the aerodynamic efficiency of gliding birds, there is no comprehensive summary of these results. This lack of consolidated information inhibits a true comparison between birds and UAVs. Such a comparison is complicated by variable uncertainty levels between the different techniques used to predict avian efficiency. To support our comparative approach, we began by surveying theoretical and experimental estimates of avian aerodynamic efficiency and investigating the uncertainty associated with each estimation method. We found that the methodology used by a study affects the estimated efficiency and can lead to incongruent conclusions on gliding bird aerodynamic efficiency. Our survey showed that studies on live birds gliding in wind tunnels provide a reliable minimum estimate of a birds' aerodynamic efficiency while simultaneously quantifying the wing configurations used in flight. Next, we surveyed the aeronautical literature to collect the published aerodynamic efficiencies of similar-sized, non-copter UAVs. The compiled information allowed a direct comparison of UAVs and gliding birds. Contrary to our expectation, we found that there is no definitive evidence that any gliding bird species is either more or less efficient than a comparable UAV. This non-result highlights a critical need for new technology and analytical advances that can reduce the uncertainty associated with estimating a gliding bird's aerodynamic efficiency. Nevertheless, our survey indicated that species flying within subcritical Reynolds number regimes may inspire UAV designs that can extend their operational range to efficiently operate in subcritical regimes. The survey results provided here point the way forward for research into avian gliding flight and enable informed UAV designs.

Bioinspired wing and tail morphing extends drone flight capabilities

The aerodynamic designs of winged drones are optimized for specific flight regimes. Large lifting surfaces provide maneuverability and agility but result in larger power consumption, and thus lower range, when flying fast compared with small lifting surfaces. Birds like the northern goshawk meet these opposing aerodynamic requirements of aggressive flight in dense forests and fast cruising in the open terrain by adapting wing and tail areas. Here, we show that this morphing strategy and the synergy of the two morphing surfaces can notably improve the agility, maneuverability, stability, flight speed range, and required power of a drone in different flight regimes by means of an avian-inspired drone. We characterize the drone's flight capabilities for different morphing configurations in wind tunnel tests, optimization studies, and outdoor flight tests. These results shed light on the avian use of wings and tails and offer an alternative design principle for drones with adaptive flight capabilities.

Rapid two-anchor crawling from a milliscale prismatic-push-pull (3P) robot

Many crawling organisms such as caterpillars and worms use a method of movement in which two or more anchor points alternately push and pull the body forward at a constant frequency. In this paper we present a milliscale push-pull robot which is capable of operating across a wide range of actuation frequencies thus enabling us to expand our understanding of two-anchor locomotion beyond the low-speed regime. We designed and fabricated a milliscale robot which uses anisotropic friction at two oscillating contact points to propel itself forward in a push-pull fashion. In experiments we varied the oscillation frequency,f, over a wide range (10-250 Hz) and observe a non-linear relationship between robot speed over this full frequency range. At low frequency (f< 100 Hz) forward speed increased linearly with frequency. However, at an intermediate push-pull frequency (f> 100 Hz) speed was relatively constant with increasing frequency. Lastly, at higher frequency (f> 170 Hz) the linear speed-frequency relationship returned. The speed-frequency relationship at low actuation frequencies is consistent with previously described two-anchor models and experiments in biology and robotics, however the higher frequency behavior is inconsistent with two-anchor frictional behavior. To understand the locomotion behavior of our system we first develop a deterministic two-anchor model in which contact forces are determined exactly from static or dynamic friction. Our experiments deviate from the model predictions, and through 3D kinematics measurements we confirm that ground contact is intermittent in robot locomotion at higher frequencies. By including probabilistic foot slipping behavior in the two-anchor friction model we are able to describe the three-regimes of robot locomotion.