Ship maneuvering toward a dock is a hot research topic in the field of autonomous ships. How to realize autonomous low-speed maneuver to a designated location under environmental disturbances is the fundamental problem at present. In addition, potential collisions with other nearby vessels and evasive maneuvers that meet maritime regulations increase the complexity of maneuvering. This paper presents an effective ship control strategy for collision-free maneuver toward a dock. In the strategy, a line-ofsight (LOS) algorithm is utilized to steer the ship along a pre-planned path toward the dock. A collision risk factor that takes ship's maneuvering characteristics, ship domain and relative velocity into account is designed for collision avoidance. Evasive maneuvers complying with the main rules of the international collision avoidance regulations at sea can be achieved based on the risk factor. A ship bumper is constructed, based on which the strategy can switch to either "path following" or "collision avoidance". Numerical simulations for different encounter situations including head-on, starboard-crossing and overtaking under wind perturbations are carried out. The results show the proposed strategy is able to keep the ship domain free during maneuvering and steer the ship to the docking area in a safe and appropriate manner.
Since sow backfat thickness (BFT) is highly correlated with its service life and reproductive effectiveness, dynamic monitoring of BFT is a critical component of large‐scale sow farm productivity. Existing contact measures of sow BFT have their problems including, high measurement intensity and sows' stress reaction, low biological safety, and difficulty in meeting the requirements for multiple measurements. This article presents a two‐dimensional (2D) image‐based approach for determining the BFT of pregnant sows when combined with the backfat growth rate (BGR). The 2D image features of sows extracted by convolutional neural networks (CNN) and the artificially defined phenotypic features of sows such as hip width, hip height, body length, hip height–width ratio, length–width ratio, and waist–hip ratio, were used respectively, combined with BGR, to construct a prediction model for sow BFT using support vector regression (SVR). Following testing and comparison, it was shown that using CNN to extract features from images could effectively replace artificially defined features, BGR contributed to the model's accuracy improvement. The CNN‐BGR‐SVR model performed the best, with R2 of 0.72 and mean absolute error of 1.21 mm, and root mean square error of 1.50 mm, and mean absolute percentage error of 7.57%. The results demonstrated that the CNN‐BGR‐SVR model based on 2D images was capable of detecting sow BFT, establishing a new reference for non‐contact sow BFT detection technology.
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