Sensing surfaces like a mosquito: Although sonar or lidar are used by autonomous vehicles to detect nearby objects, these approaches incur significant equipment and signal-processing costs. Nakata et al. show that mosquitos detect surfaces using the flow fields caused by the movement of their own wings (see the Perspective by Young and Garratt). Near surfaces, there are small changes in the pressure and velocity that mosquitos can detect using their sensitive antennae. The authors translated this process into a simple, low-cost approach for detecting surfaces near a flying quadcopter. Science , this issue p. 634 ; see also p. 586

Precise prediction of unsteady flapping aerodynamics in insect flight is of potential importance in the analysis of maneuverability and flight control. While the quasi-steady model is a cheap while reasonable tool, accurate evaluation of unsteady dynamic effects in complex flight behaviours remains a challenge. Here we develop a computational fluid dynamics (CFD) data-driven aerodynamic model (CDAM), which is informed by high-fidelity CFD simulations using overset meshes to enable the precise and fast prediction of both cycle-averaged and transient aerodynamic force, torque and power with various flying motions and wing kinematics. The CDAM comprises a quasi-steady model for flapping wings and an aerodynamic model for a moving body. The least square method and a surrogate method are employed to achieve aerodynamic coefficient fitting through training using a CFD database. With comparison to CFD test data, the CDAM is validated to be capable of accurately evaluating the aerodynamic force, torque and power of a wing-body bumblebee model in various flight velocities. A genetic optimization algorithm embedded with CDAM is proposed to determine trimmed states for forward flight through adjusting wing kinematics, indicating that bumblebees likely fly in a minimized mass-specific aerodynamic power consumption. The CDAM is further applied to proportional-derivative-based longitudinal flight control of bumblebee hovering, with the control parameters optimized by Laplace transformation and the root locus method, which is implemented consistently in both CDAM and CFD environments. Our results demonstrate that CDAM provides a versatile tool to achieve fast and precise aerodynamical prediction for flying insects in various flight behaviours.

The utilization of small unmanned aerial vehicles (SUAVs), commonly known as drones, has increased drastically in various industries in the past decade. Commercial drones face challenges in terms of safety, durability, flight performance, and environmental effects such as the risk of collision and damage. Biomimetics, which is inspired by the sophisticated flying mechanisms in aerial animals, characterized by robustness and intelligence in aerodynamic performance, flight stability, and low environmental impact, may provide feasible solutions and innovativeness to drone design. In this paper, we review the recent advances in biomimetic approaches for drone development. The studies were extracted from several databases and we categorized the challenges by their purposes—namely, flight stability, flight efficiency, collision avoidance, damage mitigation, and grasping during flight. Furthermore, for each category, we summarized the achievements of current biomimetic systems and then identified their limitations. We also discuss future tasks on the research and development associated with biomimetic drones in terms of innovative design, flight control technologies, and biodiversity conservation. This paper can be used to explore new possibilities for developing biomimetic drones in industry and as a reference for necessary policy making.

Unlike rigid rotors of drones, bird wings are composed of flexible feathers that can passively deform while achieving remarkable aerodynamic robustness in response to wind gusts. In this study, we conduct an experimental study on the effects of the flexible flaps inspired by the covert of bird wings on aerodynamic characteristics of fixed-wings in disturbances. Through force measurements and flow visualization in a low-speed wind tunnel, it is found that the flexible flaps can suppress the large-scale vortex shedding and hence reduce the fluctuations of aerodynamic forces in a disturbed flow behind an oscillating plate. Our results demonstrate that the stiffness of the flaps strongly affects the aerodynamic performance, and the force fluctuations are observed to be reduced when the deformation synchronizes with the strong vortex generation. The results point out that the simple attachment of the flexible flaps on the upper surface of the wing is an effective method, providing a novel biomimetic design to improve the aerodynamic robustness of small-scale drones with fixed-wings operating in unpredictable aerial environments.

In city-wide weather prediction, wind gust information can be obtained using unmanned aerial vehicles (UAVs). Although wind sensors are available, an algorithm-based active estimation can be helpful not only as a weightless substitute but also as feedback for robust control. This paper aims to estimate the wind gusts affecting the quadrotors (a type of UAV) as the input disturbances by using a frequency-based nonlinear disturbance observer (NDOB). To obtain highly accurate estimations, frequency is considered as the main design parameter, thereby focusing the estimation on the frequency range of the wind gusts. The NDOB is developed using the Takagi-Sugeno (T-S) fuzzy framework. In this approach, the twelfth-order nonlinear model is approximated into a sixth-order T-S fuzzy model to reduce computational cost. A two-step verification method is presented, which includes MATLAB/Simulink simulations and the experiments performed using a 2.5 kg quadrotor.