Birds achieve remarkable flight performance by flexibly morphing their wings during different flight stages. However, due to the lack of experimental data on the free morphing of wings and the complexity of coupled motion in aerodynamics studies, the intricate kinematic changes and aerodynamic mechanisms of wings during various flight stages still need to be explored. To address this issue, we collected comprehensive data on free-flight pigeons (Columba livia). We categorized the wing kinematic parameters during the takeoff, leveling flight, and landing stages into 5 kinematics parameters: flap, twist, sweep, fold, and bend. Based on this, we established a 3-dimensional pigeon wing model, defined its coupled motion using rotation matrices, and then used the computational fluid dynamics method to simulate the coupled motion in the 3 flight stages. We analyzed and compared the kinematic parameter changes, aerodynamic forces, and flow structures. It is found that, within a wingbeat cycle, pigeons during the takeoff stage cause the leading-edge vortex to attach earlier, enhancing instantaneous lift to overcome gravity and achieve ascending. During the leveling flight stage, the pigeon’s average lift becomes stable, ensuring a steady flight posture. In the landing stage, the pigeon increases the wing area facing the airflow to maintain a stable landing posture, achieving a more minor, consistent average lift while increasing drag. This study enhances our understanding of birds’ flight mechanisms and provides theoretical guidance for developing efficient bio-inspired flapping-wing aerial vehicles.

Large quadruped robots have shown potential for a wide range of everyday tasks due to their superior terrain adaptation. However, small-scale quadruped robots have limited payloads and thus cannot carry sufficient sensing and computational resources, which imposes limitations on their environmental adaptability. To address this challenge, we proposed an efficient closed-loop adaptive controller by simplified pose estimation and control strategy that utilizes only inertial measurement unit sensors, which drastically reduces the control computation. Accordingly, we integrated this control system into a small-scale quadruped robot, SQuRo, and conducted a series of experiments to verify the environmental adaptation performance of SQuRo. The results demonstrated that SQuRo has achieved 6 kinds of robust motion: slope stabilization motion, linear tracking motion, autonomous fall recovery motion, uneven terrain walk, slope walk, and obstacle avoidance. This work paves the way for small-scale quadruped robots to autonomously perform tasks in challenging environments.

In this paper, we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning. Specifically, we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions. The novelty of our method lies in using the temporal difference (TD) algorithm to optimize the state decision process, which enables the robots to make informed decisions about their behavior choices. To assess the similarity between robot and rat behavior, we use Pearson correlation. We then use TD-λ to update the state value function and make state decisions based on probability. The robots execute these decisions using our dynamics-based controller. Our results demonstrate that our method can generate rat-like behaviors on both short- and long-term timescales, with interaction information entropy comparable to that between real rats. Overall, our approach shows promise for controlling robots in robot–rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.