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Since the unmanned sailboat is difficult to obtain the true wind, and the wind sensor used is affected by the attitude of the sailboat, a calculation method of the true wind of the unmanned sailboat based on the fusion of attitude information, relative wind, and sailing wind was proposed. Firstly, the pitch angle and roll angle of the sailboat were obtained by using the attitude instrument, and the relative wind measurement results were corrected by the spatial coordinate rotation and transformation. Secondly, the Kalman filter algorithm was used to filter the sailing wind and the corrected relative wind. Finally, the extended Kalman filter was utilized to perform true wind fusion calculations. Experiments have proved that this method can effectively suppress the influence of sailboat motion on wind measurement, accurately extract true wind speed and wind direction, and ensure the sailing control of unmanned sailboats.

Abstract Measuring and reconstructing the shape of workpieces have been considered as a fundamental step in both reverse engineering and product quality control. Owing to increasing structural complexity of recent products, measurements from multiple directions are typically required in current scanning techniques. Specifically, the plane structured light can be applied to measure one area of a part at a time, with an additional algorithm required to merge the collected data of each area. Alternatively, the line structured light sensor integrated on CNC machines or CMMs could also realize multi-view measurement. However, the system needs to be repeatedly calibrated at each new direction. This paper presents a flexible scanning method by integrating laser line sensors with articulated arm coordinate measuring machines (AACMM). Since the output of the laser line sensor is 2D raw data in the laser plane, our system model introduces an explicit transformation from the 2D sensor coordinate frame to the 3D base coordinate frame of the AACMM (i.e., the translation and rotation the of the 2D sensor coordinate in the sixth coordinate system of AACMM). To solve the model, the “conjugate pairs” are proposed and identified by measuring a fixed point (e.g., a sphere center). Moreover, a search algorithm is adopted to find the optimal solution, which noticeably boosts the model accuracy. The experimental results show that the error of the system is about 0.2 mm, which is caused by the error of the AACMM, the sensor error and the calibration error. By measuring a complicated part, the proposed system is proved to be flexible and facilitate, with the ability to measure a part expediently from any necessary direction. Furthermore, the proposed calibration method can also be used for robot hand-eye relationship calibration.

Autonomous sailing robots are a new type of green ship that use wind energy to maintain continuous cruising operations. Compared with traditional algorithms, swarm intelligence optimization algorithms have better intelligence and adaptation. An intelligent algorithm acts as one of the most important solutions to the path planning problem of autonomous sailing robots. The beetle swarm optimization, which is a novel intelligent method that combines the search mechanism of a single beetle with the particle swarm optimization algorithm, is utilized to obtain the optimal path. In this study, the track navigation control of an improved mathematical model of a sailing ship is introduced, and the navigation is tested using a downsized prototype of an autonomous sailing robot. The improved beetle swarm optimization is proposed here by dynamically changing the step size factor and the inertia weight formula. In the iteration of the improved beetle swarm optimization algorithm, the location update cooperates with the beetle monomer search mechanism to learn the update strategy of the particle swarm optimization algorithm. Combinatorial strategies can speed up the overall iterative convergence speed and reduce the possibility that the population will fall into a locally optimal solution. The simulation results demonstrate the robustness, efficiency, and feasibility of the improved beetle swarm optimization in different cases. The research results can provide some references and ideas for the autonomous intelligent navigation control design of autonomous sailing robots.

Whitecap formation is an important factor in the exchange of momentum, heat, and gas on the ocean surface. The long-term measurement of whitecaps is necessary to deepen our understanding of the mechanisms of ocean surface motion. However, traditional detection methods are highly sensitive to illumination. Under various illumination conditions, significant light pollution may be introduced into images. The poor performance caused by using images degraded with light pollution is not conducive to automated long-term whitecap measurement. In this study, we propose a two-step method for the detection of whitecaps under various illumination conditions. An abnormal detection method based on previous whitecap detection methods for the accurate detection of whitecaps in light-polluted areas is proposed as the first step. Using the detection results, we propose a post-processing method based on optical flow trajectories at two sampling rates to separate actual whitecap components in images containing false positives. Experiments show that the method proposed in this study can more accurately detect whitecaps in images with light pollution when compared to existing methods.

Due to the complexity and variability of the underwater acoustic channel, the communication signal is affected by multi-path, time delay, and Doppler frequency shift. Based on the advantageous characteristics of fractional Fourier transform on chirp signal processing, a fractional Fourier transform–based algorithm using combined linear frequency–modulated signal is proposed, which can estimate parameters of underwater acoustic channel and has a better performance than the existing methods. To distinguish multi-user in underwater acoustic communication system, a single-carrier direct sequence code division multiple access communication system combined with the fractional Fourier transform–based algorithm is proposed. Thus, a preliminary study on underwater multi-target identification is carried out. The simulation and experimental results show that the fractional Fourier transform–based algorithm is simple and effective, and the energy can be focused at the “best” fractional order, which can directly determine the multi-path number and complete the channel estimation. The proposed single-carrier direct sequence code division multiple access communication system has good performance on bit error rate when we use corresponding spreading code to distinguish multi-user.

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

In this study, a laser line auto-scanning system was designed to perform underwater close-range 3D reconstructions with high accuracy and resolution. The system changes the laser plane direction with a galvanometer to perform automatic scanning and obtain continuous laser strips for underwater 3D reconstruction. The system parameters were calibrated with the homography constraints between the target plane and image plane. A cost function was defined to optimize the galvanometer’s rotating axis equation. Compensation was carried out for the refraction of the incident and emitted light at the interface. The accuracy and the spatial measurement capability of the system were tested and analyzed with standard balls under laboratory underwater conditions, and the 3D surface reconstruction for a sealing cover of an underwater instrument was proved to be satisfactory.