Underwater pipelines are the channels for oil transportation in the sea. In the course of pipeline operation, leakage accidents occur from time to time for natural and man-made reasons which result in economic losses and environmental pollution. To avoid economic losses and environmental pollution, damage detection of underwater pipelines must be carried out. In this paper, based on the histogram of oriented gradient (HOG) and support vector machine (SVM), a non-contact ultrasonic imaging method is proposed to detect the shedding damage of the metal underwater pipeline external anti-corrosion layer. Firstly, the principle of acoustic scattering characteristics for detecting the metal underwater pipelines is introduced. Following this, a HOG+SVM image-extracting algorithm is used to extract the pipeline area from the underwater ultrasonic image. According to the difference of mean gray value in the horizontal direction of the pipeline project area, the shedding damage parts are identified. Subsequently, taking the metal underwater pipelines with three layers of polyethylene outer anti-corrosive coatings as the detection object, an Autonomous Surface Vehicle (ASV) for underwater pipelines defect detection is developed to verify the detection effect of the method. Finally, the underwater ultrasonic image which used to detect the metal underwater pipeline shedding damage is obtained by acoustic sensor. The results show that the shedding damage can be detected by the proposed method. With the increase of shedding damage width, the effect of pipeline defect location detection is better.

The autonomous driving is rapid developing recently and model predictive controls (MPCs) have been widely used in unmanned vehicle trajectory tracking. MPCs are advantageous because of their predictive modeling, rolling optimization, and feedback correction. In recent years, most studies on unmanned vehicle trajectory tracking have used only linear model predictive controls to solve MPC algorithm shortcomings in real time. Previous studies have not investigated problems under conditions where speeds are too fast or trajectory curvatures change rapidly, because of the poor accuracy of approximate linearization. A nonlinear model predictive control optimization algorithm based on the collocation method is proposed, which can reduce calculation load. The algorithm aims to reduce trajectory tracking errors while ensuring real-time performance. Monte Carlo simulations of the uncertain systems are carried out to analyze the robustness of the algorithm. Hardware-in-the-loop simulation and actual vehicle experiments were also conducted. Experiment results show that under i7-8700, the calculation time is less than 100 ms, and the mean square error of the lateral deviation is maintained at ${10}^{-3} {\mathrm{m}}^2$, which proves the proposed algorithm can meet the requirement of real time and accuracy in some particular situations. The unmanned vehicle trajectory tracking method provided in this article can meet the needs of real-time control.

The safety of unmanned ship berthing is of paramount importance. In order to explore the influence of wind and wave coupling, a berthing computational fluid dynamics (CFD) model was established, and the characteristics of speed field, pressure field, and vortex have been obtained under different speed, wind direction, and the quay wall distances. The results show that the total resistance of the hull against the current can be about 1.60 times higher compared to the downstream resistance, water flow resistance is the dominant factor, accounting for more than 80% of the total resistance. When changing the distance between ship and shore at fixed speed, the results found that the torque is small, but the growth rate is very large when driving below 2 m/s, and the torque growth rate is stable above 2 m/s. Based on the established coupling model, a multi-factor berthing safety study is carried out on an actual unmanned ship. The results show that when the speed increases from 4 m/s to 12 m/s, the curve slope is small, the resistance increases from 3666 N to 18,056 N, and the rear slope increases. The pressure increases with the speed, and when the speed is 24 m/s, the maximum pressure is up to 238,869 Pa. When the wind speed is fixed, the vertical force of the unmanned ship increases first and then decreases to zero and then reverses the same law change, and the maximum resistance is about 425 N at the wind angle of about 45 degrees; At 90 degrees, the maximum lateral force on an unmanned boat is about 638 N. The above results can provide control strategy for unmanned ship berthing safety, and provide theoretical basis for unmanned ship route planning and obstacle avoidance, safety design, etc.

In response to the urgent need for quantitative evaluation of the obstacle avoidance performance of an unmanned ship, a quantitative evaluation model is established to evaluate quantitatively the obstacle perception performance, static obstacle avoidance performance and dynamic obstacle avoidance performance of an unmanned ship. The base data for calculating are derived from the shore-based database; the evaluation factor layer is evaluated by the cost function method. Based on the established evaluation model, the quantitative evaluation score of the obstacle perception performance of the unmanned ship is obtained by data analysis for the 50 to 100 m buoy and 100 m island obstacle perception test. The quantitative evaluation score of the static obstacle avoidance performance obtained by testing the performance of a single obstacle, continuous obstacle and inflection obstacle is 68.8 points. For buoys as dynamic obstacles, the dynamic obstacle avoidance performance quantitative evaluation score of 64.13 points is obtained by testing the performance of obstacle avoidance in chasing, facing and crossing encounters. The analysis of the test data saved to the database verify the rationality of the quantitative evaluation model, which can provide reference for the quantitative evaluation and improvement of the unmanned ship’s obstacle avoidance performance.

This work develops a heat transfer model of a thermoelectric generator to explore the coupled relationship between high temperature exhaust flows, structure, and the external cooling air. The coupled heat transfer results showed that the fins reached a uniform high temperature, for the rated speed, the average temperature is 474 K. The coupled design scheme of the thermoelectric generator tested by installing it in a 4-cylinder turbocharged Diesel engine exhaust system. Comparison of the test and simulation results showed that as engine speed in-creased, the inlet and outlet exhaust temperatures of the thermoelectric generator exhibited a parabolic trend increase. The cooling water outlet temperature and the top, middle, and bottom fin temperatures increased linearly, and the coupled model was verified. From idle speed to rated speed, the top, middle, and bottom fin temperatures increased from 458 K to 476 K, 417 K to 463 K, and 406 K to 449 K, respectively; the cooling water outlet temperature increased from 293.6 K to approximately 303 K. Hence, the thermoelectric components installed in fins can experience temperature differences of over 100 K, the heat transfer efficiency can increase which ensures consistent output performance of the thermoelectric generator based on coupled design between the heat exchanger and thermoelectric modules array column.

An overall framework of the virtual testing system has been established based on the analysis of the virtual testing requirements for autonomous navigation performance of unmanned surface vehicles (USVs). This system consists of several modules, including the environment module, motion module, sensor module, and 3D visualization module. Firstly, within the robot operating system (ROS) environment, a three-dimensional navigation environment was generated by combining actual wave spectra with Gerstner waves. By designing a power plugin for USV navigation, the system was made to reflects the coupled motion model of USVs in wind, waves and currents, along with predictive results. Regarding the four typical sensor information on USVs, the actual sensors were virtualized, and a simulation approach for virtual sensor information is provided. The three-dimensional visualization of USV’s motion enables the intuitive display and analysis of the virtual testing process. Based on the prediction of coupled motion characteristics in wind, waves and currents, the interaction between USVs and the virtual testing system has been realized. A platform for virtual testing experiments to determine the autonomous navigation performance of USVs was established, and the effectiveness of the platform was verified in terms of perception and environmental interference. In virtual environmental interference validation, the average amplitude deviation of the heave motion of USVs under sea state 3 reaches 0.74 m, and the average amplitude deviation of the pitch motion reaches 0.25 rad, showing the gradually increasing disturbance of the sea state. Finally, virtual testing experiments were conducted on a specific USV to evaluate its autonomous navigation perception performance, trajectory tracking performance, and autonomous obstacle avoidance. The evaluation results indicate that the platform can achieve the functionality of virtual testing for the autonomous navigation performance of USVs from the perspective of cost function, taking the reaction distance, regression distance, and obstacle avoidance time into consideration. A representative example is that the cost function deviation rates of overtaking obstacle avoidance between static and dynamic seas reach 5.11%, 8.98% and 18.43%, respectively. The gradually increasing data shows that the virtual simulating method matches the drifting-off-course tendency of boats in rough seas. This includes acquiring perception information of navigation and simulating the motion and navigation processes for visualization. The platform provides new means for testing and evaluating the autonomous navigation performance of USVs.