Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the “XL” USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

A novel method of bionic Morse coding mimicking humpback whale vocal is presented for covert underwater acoustic communication. The complex humpback whale song is translated as bionic Morse codes based on information entropy. The communication signal is made akin to the natural singing of male humpback whales. The intruder can detect the signal but will not be able to recognize the communication signal due to unified resemblance with the natural sound. This novel technique gives an excellent low probability of recognition characteristics. A flawless stealthy underwater acoustic communication has been established which has negligible chances of deciphered with high imperceptibility. Standard mimicry Morse codes have been developed for the characters of the English language and compared with Morse coding. Covert information of one character per second can be watermarked with perfect stealth and clandestine communication. This novel concept has been verified at transmission distance of five km and less than 10⁻³ Bit Error Rate (BER) is achieved at Signal to Noise Ratio (SNR) down to negative seven dB. Zero BER is attained by estimating the channel by a matching pursuit algorithm and equalizing the errors by virtual time reversal mirror technique.

The aim of this study is to address the trajectory-tracking control problem of benthic autonomous underwater vehicles (AUVs) subjected to model uncertainties and extra disturbance. In order to estimate lumped uncertainties and reconstruction speed information, this paper designs a robust prescribed-time extended state observer (RPTESO), and its prescribed time can be directly designed as an explicit parameter, without relying on the initial state of the system and complex parameter settings. In addition, an adaptive law is designed to improve the robustness of RPTSEO and reduce overshoot on the premise of ensuring convergence speed. Then, a non-singular robust practical predefined-time sliding mode control (RPPSMC) considering the hydrodynamic characteristics of AUV is designed, and the predefined time can be directly set by an explicit parameter. The RPPSMC is designed based on the lumped uncertainties estimated using RPTESO, so as to improve the control accuracy of the controller in a complex environment. Theoretical analysis and simulations demonstrated the effectiveness and superiority of the proposed method.

This paper presents an uncalibrated visual servoing scheme for underwater vehicle manipulator systems (UVMSs) with an eye-in-hand camera under uncertainties. These uncertainties contain vision sensor parameters, UVMS kinematics and feature position information. At first, a linear separation approach is addressed to collect these uncertainties into vectors, and this approach can also be utilized in other free-floating based manipulator systems. Secondly, a novel nonlinear adaptive controller is proposed to achieve image error convergence by estimating these vectors, the gradient projection method is utilized to optimize the restoring moments. Thirdly, a high order disturbance observer is addressed to deal with time-varying disturbances, and the convergence of the image errors is proved under the Lyapunov theory. Finally, in order to illustrate the effectiveness of the proposed method, numerical simulations based on a 9 degrees of freedom (DOFs) UVMS with an eye-in-hand camera are conducted. In simulations, the UVMS is expected to track a circle trajectory on the image plane, meanwhile, time-varying disturbances are exerted on the system. The proposed scheme can achieve accurate and smooth tracking results during simulations.

This study focuses on addressing the the coupling problem of the vertical and horizontal plane of an autonomous underwater vehicle (AUV) with an X-rudder. To guarantee the steering performance of the AUV, a depth and course control algorithm based on an $L_1$ adaptive control algorithm (L1AC) is proposed. Firstly, the linear quadratic regulator (LQR) is designed on the basis of an AUV vertical and horizontal linear model, and the system has optimal control quality for both of the nominal vertical and horizontal models. Secondly, the nonlinear state predictor, disturbance estimator, and adaptive controller are designed separately, which is used for the compensation of the uncertainty and disturbance of the AUV dynamic. Finally, the rudder angle allocation algorithm is designed to realize the rudder angle command of the cross rudder to the X-rudder. The simulation and experiment results show that the adaptive control algorithm proposed in this paper can effectively control AUV to complete high-performance course and depth control. Based on the experiments, the prospects and shortcomings of the L1AC, LQR and MRAC algorithm in AUV control are compared and analyzed.