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As a mobile autonomous ocean observation platform, Autonomous Underwater Vehicle (AUV) has the advantage of long endurance and large range, being free from sea condition. The microstructure of velocity and temperature of horizontal and vertical profiles can be obtained with a selfdeveloped AUV, which carries two shear probes, and an FP07 thermistor mounted on a sting at the forward end of the vehicle. However, since the shear probes are essentially transverse force sensors, they are sensitive to vibration noise, and any vibration of the AUV will show up as a spurious signal in the measured shear signal, which will reduce the lower resolution limit of dissipation rate of turbulent kinetic energy. Focusing on the vibration noise of AUV turbulence measurement platform, the work has presented a flexible connection vibration reduction method based on extension spring, and the vibration test has been carried out. By comparing and analyzing the time-frequency domain characteristics of vibration acceleration before and after vibration damping, the results show that the RMS and energy of vibration acceleration are significantly reduced, especially vibration above 54 Hz, whereupon the average damping efficiency is more than 70%, which verifies the validity of this method.

This paper presents a novel approach about constraining trajectories of underwater gliders based on Deep Q Network(DQN) and the dynamic model of gliders. In terms of dynamics modeling, we choose the angle of attack and roll angle as the input of the glider' movement simulation, the profile motions in the vertical plane is manipulated by the angle of attack, and the gyratory motion is accomplished by controlling the roll angle. Since different roll angles allow the glider to turn with different radius, it can be used to counteract the influence of current on the motion state of the glider. And the determination of the proper roll angle to make the glider's trajectory consistent with the preset route is a sequential decision problem. We use the glider's motion space discretization and apply the deep reinforcement learning theory to acquire the optimal roll angle, thus finally achieve the goal that improves the accuracy of the glider's trajectory. The simulation results show that the proposed method has better performance and advantage to deploy compared with the conventional PID method.