State Key Laboratory of Ocean Engineering, Department of Engineering Mechanics, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Nat Commun. 2024 Oct 22;15(1):9099. doi: 10.1038/s41467-024-53517-6.
Avian feather intricate adaptable architecture to wing deformations has catalyzed interest in feathered flapping-wing aircraft with high maneuverability, agility, and stealth. Yet, to mimic avian integrated somatic sensation within stringent weight constraints, remains challenging. Here, we propose an avian-inspired embodied perception approach for biohybrid flapping-wing robots. Our feather-piezoelectric mechanoreceptor leverages feather-based vibration structures and flexible piezoelectric materials to refine and augment mechanoreception via coupled oscillator interactions and robust microstructure adhesion. Utilizing convolutional neural networks with the grey wolf optimizer, we develop tactile perception of airflow velocity and wing flapping frequency proprioception. This method also senses pitch angle via airflow direction and detects wing morphology through feather collisions. Our low-weight, accurate perception of flapping-wing robot flight states is validated by motion capture. This investigation constructs a biomechanically integrated embodied perception system in flapping-wing robots, which holds significant promise in reflex-based control of complex flight maneuvers and natural bird flight surveillance.
鸟类羽毛复杂、适应性强的结构能够适应翅膀变形,这激发了人们对具有高机动性、敏捷性和隐身性的羽毛扑翼飞机的兴趣。然而,要在严格的重量限制下模拟鸟类综合体感,仍然具有挑战性。在这里,我们提出了一种受鸟类启发的生物混合扑翼机器人的体现感知方法。我们的羽毛-压电机械感受器利用基于羽毛的振动结构和柔性压电材料,通过耦合振荡器相互作用和稳健的微观结构粘附来改进和增强机械感知。我们利用卷积神经网络和灰狼优化器,开发了对气流速度和翅膀拍打频率本体感觉的触觉感知。该方法还可以通过气流方向感知俯仰角,并通过羽毛碰撞检测翅膀形态。我们通过运动捕捉验证了扑翼机器人飞行状态的低重量、精确感知。这项研究构建了一个在扑翼机器人中具有生物力学集成体现感知系统,这在复杂飞行机动的基于反射的控制和自然鸟类飞行监测方面具有重要意义。