Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically.

For multi-obstacle complex scenarios, the traditional artificial potential field method suffers from the defects of potential field imbalance, its capability to easily fall into the local minima, and encounter unreachable targets in complex navigation environments. Therefore, this paper proposes a three-dimensional adaptive potential field algorithm (SAPF) based on multi-agent reinforcement learning. First, in this paper, the gravitational function in the artificial potential field (APF) is modified to weaken the gravitational effect on the UAV in the region far away from the target point in order to reduce the risk of collision between the UAV and the obstacles during the moving process. Second, in the region close to the target point, this paper improves the artificial potential field function to ensure that the UAV can reach the target point smoothly and realize path convergence by considering the relative distance between the UAV’s current position and the target point. Finally, for the characteristics of UAV trajectory planning, a 3D state space is designed based on the 3D coordinates of the UAV, the distance between the UAV and the nearest obstacle, and the distance between the UAV and the target point; an action space is designed based on the displacement increment of the UAV in the three coordinate axes; and the specific formulas for collision penalties and path optimization rewards are re-designed, which effectively avoids the UAV from entering the local minimal points. The experimental results show that the artificial potential field method designed with reinforcement learning can plan shorter paths and exhibit better planning results. In addition, the method is more adaptable in complex scenes and has better anti-interference.

As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV landing platform to address the impact of complex sea conditions on the stability of UAV landing platforms. Firstly, the parallel Stewart platform was introduced as the landing platform, and its structure was analyzed with forward and inverse kinematic calculations conducted in Matlab to verify its accuracy. Secondly, a least-squares recursive AR prediction algorithm was designed to predict the future attitudes of ships under varying sea conditions. Finally, the prediction algorithm was combined with the platform’s control strategy and a dual-sensor system was adopted to ensure the stability of the UAV landing process. The experimental results demonstrate that these innovative improvements enhanced the compensation accuracy by 59.6%, 60.3%, 48.4%, and 47.9% for the rolling angles of 5° and 10° and the pitching angles of 5° and 10°, respectively. Additionally, the compensation accuracy for the roll and pitch in sea states 2 and 5 improved by 51.2%, 59.4%, 58.7%, and 55.9%, respectively, providing technical support for UAV missions such as maritime rescue and exploration.

Aiming at the problems of insufficient navigation area recognition accuracy, fuzzy boundary of obstacle segmentation, and high consumption of computational resources in the autonomous navigation of water navigation sensors, such as USVs, this paper proposes a DMTN-Net network architecture based on DeeplabV3+ to improve the accuracy and efficiency of environment sensing. Firstly, DMTN-Net adopts the lightweight MobileNetV2 as the backbone, which reduces the amount of computation. Secondly, the innovative N-Decoder structure integrates cSE and Triplet Attention, which enhances the feature representation and improves the segmentation performance. Finally, various experiments were conducted on the MassMind dataset, Pascal VOC2007 dataset, and related sea areas. The experimental results show that DMTN-Net performs well on MassMind and Pascal VOC2007 datasets, and compared with other mainstream networks, the indexes of mIoU, mPA, and mPrecision are significantly improved, and the computational cost is greatly reduced. In addition, the offshore navigation experiments further validate its performance advantages and provide solid support for the practicalization of USV waterborne sensors.

The demand for non-powered facility towing is increasing with the development of large-scale offshore projects. It is of great interest for its safe operation to measure the state of the towing process in real time. This paper proposed a computer vision algorithm designed to measure the tug yawing during the towing operation by estimating the towing line angle. The geometrical projection of the towing line from 3D to 2D is described in detail. By fixing the camera at specific locations and simplifying the calculation procedure, the towing line angle in the 3D world can be estimated by the line angle in the image. Firstly, the sea–sky line is detected to estimate the rolling angle of the tug in the captured image. Then, the towing line angle is calculated by an image processing method. At the same time, the estimation of the towing angle is achieved through the captured video data analysis. Finally, field experiments were carried out and the results demonstrated that this method is suitable for real-time calculation of the towing angle during the towing operation.

The inconsistency between the radar image information and electronic chart information in the high latitudes can result in much difficulty on the identification of marks, which will be dangerous to the ship's navigation. By analyzing the characteristics of the projection methods of the high-latitude sea area charts, we discuss the causes for the errors of the radar image and superimposed ECDIS information. This thesis puts forward the method of subdivision of longitude in the weft to correct the Mercator projection distortion, and therefore we can get the Mercator projection which is able to meet any predetermined accuracy requirements. Based on GPS coordinates value, radar real-time location information is changed into the screen coordinates through a coordinate transformation, and we can position radar center position on the ECDIS chart accurately. In this way, we can realize the real-time, rapid, precise overlay display of radar echo and ECDIS chart.