Aggressive autonomous flight through narrow gaps presents a critical challenge in drone racing. As flapping-wing aerial vehicles (FWAVs) evolving toward autonomous navigation, avian-inspired FWAVs are significantly hindered by their single-modal flight dynamics and nonlinear underactuated characteristics, leading to the largely unexplored area of narrow gap traversal. To address this gap, this article introducesSparrowHawk, a compact autonomous flapping-wing system optimized for aerodynamic efficiency and structural rigidity. Its design enables a favorable payload-to-weight ratio, accommodating onboard sensing for external-positioning-free localization and maneuverable forward flight. By leveraging the cycle-averaged method for modeling, the system is simplified to a nonlinear time-invariant model, with kinematic and dynamic equations derived in a unified state-space formulation. For robust real-time gate detection during flight, an adaptive Hough transform-based visual algorithm is proposed, yielding pixel-space geometric parameters of gaps without any prior knowledge. The servo control framework employs a closed-loop dual-loop strategy, integrating a TECS-based altitude controller with a cascaded PID attitude controller to ensure precise position and orientation control during autonomous gap traversal. Indoor and outdoor flight experiments demonstrate thatSparrowHawkexecutes tight-radius banked turns and altitude control with a mean error of 0.345 m. The robot successfully traverses an 80 cm-diameter circular gap at speeds up to 5.6 m/s with a 60 cm wingspan, signifying the first achievement for FWAVs. Through multiple repeated trials, it achieved success rates of 80% under normal lighting and 60% under low-light conditions, significantly outperforming a human pilot, the conventional unmodified Hough circle detector, and the deep learning–based YOLOv11 algorithm. This work advances perception-driven control methodologies for FWAVs, paving the way for agile autonomous navigation in confined environments.

Most birds in nature rely on jumping for takeoff. Flapping-Wing Robots can flap and fly like birds but require an operator to take off, which are unable to generate sufficient lift to maintain flight at a low airspeed and must accelerate to take-off speed in a short time. It poses a challenge for the design of the jumping mechanism. This study is inspired by the jump-takeoff of birds and designs a simple and lightweight jumping leg, which is capable of storing and releasing energy with only one degree of freedom. In addition, a prototype was developed and tested, with a wingspan of 2 meters and a mass of 1.6 kilograms, accelerating to 4 m/s in 52 ms by jumping, achieving the jumping take-off from the ground.

Large flapping-wing aerial vehicles (FWAVs) face dual challenges in aerodynamic and structural design, with long-standing technical bottlenecks, particularly in roll maneuvers. In this study, by reverse-engineering the biomechanical mechanisms of raptor flight, we propose a bio-inspired wing-shoulder torsional mechanism and successfully developed an eagle-inspired flapping-wing aerial vehicle with a wingspan of 1.87m and a takeoff weight of 1,260g. A nonlinear explicit dynamics-lattice Boltzmann fluid-structure interaction (FSI) numerical model was innovatively established, comprehensively revealing the interaction mechanism between unsteady flapping flow fields and flexible wing deformations. Numerical simulations demonstrate that at a cruising speed of 8 m/s, the proposed mechanism generates a high-purity roll torque of 3.3 N·m (with a residual yaw torque of 0.2 N·m, torque purity ratio 16.5:1), while lift and thrust losses are below 1.5%. Flight experiments validate the exceptional performance of this mechanism in 3D maneuvers: a 360° barrel roll is completed in 2.6 seconds (average roll rate 136°/s). This study provides a theoretical framework and technological prototype for next-generation bio-inspired aerial vehicles that integrate efficient cruising with high maneuverability, marking the first instance where FWAVs surpass traditional aircraft in specific 3D maneuverability metrics.

In this work, we achieved a self-takeoff of an eagle-scale flapping-wing robot for the first time. Inspired by the takeoff process of Ospreys, we propose a bio-inspired takeoff strategy, then discuss the dynamic model and the requirements for self-takeoff. Based on the requirements of flight strategy, we designed a system with two parts, including a flapping-wing aircraft with a wingspan of 1.8m and a take-off weight of 870g, and an auxiliary platform with an initial pitch angle adjustment function. In order to explore the differences in the take-off process under different conditions, we conduct the flight experiments under different time-averaged thrust-to-weight ratios (0.745-0.876) and launch angles (45°-90°). The results of flight experiments confirmed the theoretical analysis that the flapping-wing robot can achieve self-takeoff with no potential energy cost and maintain high maneuverability (The video shows a rapid climb immediately after takeoff) even when the time-averaged thrust-to-weight ratio is smaller than 1. This is significantly different from conventional rotary-wing and vertical take-off and landing (VTOL) UAVs. This work solves the challenge of self-takeoff for large-scale flapping-wing robots using a designable method and demonstrates the superior performance potential of flapping-wing robots compared to conventional UAVs.

Large-scale flapping wing robots (FWRs) with airborne vision have important applications in visual navigation, aerial surveying, fire warning and power-line inspection. However, airborne vision and its videos suffer from strong jitters due to periodic wing flapping, which lowers the success rate of detection and measurement precision. In this paper, a robust digital video stabilization (DVS) method based on periodic jitters is proposed to provide continuous stable monitoring video without pan-tilt camera assistance. First, the periodic motion model of the FWR is established for video jitter analysis. Second, jitter frequencies in different flight states are estimated by continuous jitter acceleration. Then, feature trajectories generated from the video are adjusted adaptively for jitter frequency consistency and smoothed individually by the sampling-interpolation-averaging strategy, including the short trajectories. The stabilized video is generated by guidance from the original and smoothed trajectories. Finally, the proposed method is tested in outdoor flights with a 2.2-meter wingspan FWR and is found to outperform traditional, commercial, and deep learning DVS methods in terms of stability and robustness in various scenes and flight states.