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Fish-like robot is a special autonomous underwater vehicle with broad application prospects. Some previous studies concentrated on the hydrodynamics of free-swimming fish-like robots. But the hydrodynamic performance of fish-like robot swimming with a tilt angle in constrained space has not been well studied, and the influence of environmental wave and current on its is also still unclear. In this paper, the experiment devices, including a physical fish-like robot, a hydrodynamics measurement platform, and a six-axis force sensor, are used to study the effect of attack angle and environmental condition on the hydrodynamics of near-surface swimming fish-like robot. Nine attack angles, five oscillating amplitudes, and three environmental conditions are analyzed in the experiments. It shows that thrust force decreases when caudal fin passes above water surface, but the increased difference between gravity force and buoyancy force will compensate the decreased force generated by caudal fin when fish-like robot swims with certain dive angle. The extra reaction force generated by solid bottom boundary will promote the thrust force and vertical force. The surface water wave condition or surface water current condition also has obvious effects on hydrodynamic performance. This paper provides a new perspective to the research on the hydrodynamic performance of fish-like robot and will do favor in the development of fish-like robot.

Deep-sea exploring and sampling technologies have become frontier topics. Generally, the movable exploring mode near the seabed with low disturbance is an important way to improve the measurement accuracy and expand the measurement range. Inspired by fish, the fishlike propulsion method has the characteristics of low disturbance and high flexibility, which is very suitable for near-seabed detection under complex terrain conditions. However, the swimming mechanism and surrounding flow field evolution law of the robotic fish under the constraints of complex terrain are still unclear. In this paper, the confined terrain space is constructed with an undulating seabed and a narrow channel, and the hydrodynamic changing law and flow field evolution law of the autonomous swimming process of the fishlike swimmer in the confined space are analyzed. Moreover, the influence mechanism of the terrain on the motion performance of the robotic fish is revealed, and the optimal motion mode of the robotic fish under a complex terrain constraint is discussed. The results show that the propulsion force, Froude efficiency, and swimming stability of the robotic fish vary with the distance from the bottom under the undulating seabed condition lightly. When the distance from the bottom exceeds a certain value, it can be considered that the undulating seabed no longer affects the swimmer. Furthermore, when the robotic fish swims through a narrow channel with certain width, the swimming performance obviously varies with the distance from the boundary surface. During swimming in the confined terrain space, the propulsion force and swimming stability of robotic fish will decrease. In order to maintain the forward speed, the robotic fish should improve the tail-beat frequency in real time. However, considering the swimming stability, the tail-beat frequency is not the larger the better. The relevant conclusions of this paper could provide theoretical support for the development of low-disturbance bionic exploring and sampling platforms for deep-sea resources and environments.

How to improve the power generation of wave energy converters (WEC) has become one of the main research objectives in wave energy field. This paper illustrates a framework on the use of back propagation (BP) neural network in predicting capture power of the frustum of a cone shaped floating body. Mathematical model of single floating body is derived, and radius, semi-vertical angle, mass, submergence depth, power take-off (PTO) damping coefficient, and stiffness coefficient are identified as key variables. Commercial software ANSYS-AQWA is used for numerical simulations to obtain hydrodynamic parameters, and then capture power is calculated by these parameters. A database containing 100 samples is established by Latin hypercube sampling (LHS) method, and a simple feature study is conducted. A BP neural network model with high accuracy is designed and trained for predictions based on built database. The results show that forecasting results and desired outputs are in great agreement with error percentage not greater than 4%, correlation coefficient (CC) greater than 0.9, P value close to 1, and root mean square error (RMSE) less than 139 W. The proposed method provides a guideline for designers to identify basic parameters of the floating body and system damping coefficient.