This paper presents a novel biomimetic morphing quadrotor design inspired by the morphology of an eagle claw during prey capture. The arms of the quadrotor are capable of vertical folding to enable dynamic grasping, mimicking the transition of the eagle claw from an open to a closed state. This transition is achieved through the rotation of a central servomotor and the associated movement of 20 links. Thanks to the closed-loop multi-link structure of the frame, the propellers of the quadrotor remain in a fixed orientation when the arms are folded, allowing for system stabilization at any arm rotation angle. The geometric property of the whole frame is analyzed to determine the relationships and constraints of the links, which is important in experimental vehicle fabrication. To handle possible physical property changes and external disturbances during grasping, the adaptive sliding mode controllers are applied. To deal with objects of unknown size in grasping tasks, an admittance filter is proposed for adaptive morphology. While in flight, our proposed morphing quadrotor is able to rapidly or continuously transition to any configuration within its range smoothly. Experimental results show the ability of the quadrotor to dynamically grasp various unknown objects at 0.4m/s without additional tools, as well as its versatility in traversal of narrow spaces and perching.

Traditional methods based on Lyapunov analysis and learning-based approaches such as reinforcement learning (RL) are two powerful tools in visual servo tasks. Traditional methods are interpretable and their stability can be guar-anteed by Lyapunov analysis. However, they tend to have a high dependency on an accurate system dynamic model. RL approaches learn to act based on past experiences and thus have higher adaptability on disturbances and errors. However, the training process is long and its safety or stability is generally hard to guarantee, making real-world training risky. In this paper, we propose a residual RL framework for training a multicopter to finish visual servo tasks under disturbances, guided by the system safety in terms of system Lyapunov function. Such an approach compensates for the lack of disturbance-rejection ability of the traditional method, and optimizes stability explicitly so the RL agent makes safer actions both during the training and in the final policy. A comparison between our approach and the baselines is provided in simulation, and real-world experiments on a multicopter are also carried out to show our effectiveness. We believe that this work moves one step toward achieving RL applications on real-world robotic systems.