Kinematic analysis of leaping motions can provide meaningful insights into unraveling the efficient and agile propulsive mechanisms in dolphin swimming. However, undisturbed kinematic examination of live dolphins has been very scarce due to the restriction of close-up biological observation with a motion capture system. The main objective of this study is to quantify the leaping motion of a self-propelled bionic robotic dolphin using a combined numerical and experimental method. More specifically, a dynamic model was established for the hydrodynamic analysis of a changeable submerged portion, and experimental data were then employed to identify hydrodynamic parameters and validate the effectiveness. The effects of wave-making resistance were explored, indicating that there is a varying nonlinear relationship between power and speed at different depths. In addition, the wave-making resistance can be reduced significantly when swimming at a certain depth, which leads to a higher speed and less consumed power. Quantitative estimation of leaping motion is carried out, and the results suggest that with increase of the exiting velocity and angle, the maximum height of the center of mass (CM) increases as well; furthermore, a small exiting angle usually requires a much larger exiting velocity to achieve a complete exiting motion. These findings provide implications for optimizing motion performance, which is an integral part of underwater operations in complex aquatic environments.

This article proposes a miniature underwater angle-of-attack (AoA) sensor appropriate to self-propelled fin-actuated underwater robots, featuring real-time measurement of AoA relative to the incoming flow. Specifically, the developed sensor adopts a vane-rotating structure with a high-performance Hall chip that is responsible for detection of the angular deviation. Meanwhile, the Hall-effect-based acquisition scheme endows the device with several features, including low rotational damping, to satisfy underwater usage requirements. Another contribution is that an AoA model is derived to allow the real-time acquisition of AoA at the trailing edge of the caudal fin assumed in a quiescent flow. Based on this model, the AoA profile is obtained as theoretical reference with only the control laws required. Finally, the performance of the numerical model and the developed sensor are investigated through aquatic experiments assisted by a global visual measurement system. The experimental results demonstrate that the acquired AoA possesses over 0.97 Parson correlation coefficient with the theoretical data, verifying the effectiveness of the proposed mechatronic design. The developed AoA sensor and its application to robotic fish could provide potential assistance for the flow control and hydrodynamic optimization of fin-actuated underwater robots.

Biological fish can always sense the state of water flow and regulate the angle of attack in time, so as to maintain the highest movement efficiency during periodic flapping. The biological adjustment of the caudal fin’s angle of attack (AoA) depends on the contraction/relaxation of the tail muscles, accompanying the variation in tail stiffness. During an interaction with external fluid, it helps to maintain the optimal angle of attack during movement, to improve the propulsion performance. Inspired by this, this paper proposes a tail joint motion control scheme based on AoA feedback for the high-speed swimming of bionic dolphins. Firstly, the kinematic characteristics of the designed robot dolphin are analyzed, and the hardware basis is clarified. Second, aiming at the deficiency of the tail motor, which cannot effectively cooperate with the waist joint motor during high-frequency movement, a compensation model for the friction force and latex skin-restoring force is designed, and a joint angle control algorithm based on fuzzy inference is proposed to realize the tracking of the desired joint angle for the tail joint in torque mode. In addition, a tail joint closed-loop control scheme based on angle of attack feedback is proposed to improve the motion performance. Finally, experiments verify the effectiveness of the proposed motion control scheme.