In dense anchorage areas, the challenge of navigation for Unmanned Surface Vehicles is particularly pronounced, especially regarding path safety and economy. A Risk-Aware Path Optimization Algorithm is proposed to enhance the safety and efficiency of Unmanned Surface Vehicle navigating in anchorage areas. The algorithm incorporates risk assessment based on the A* algorithm to generate an optimized path and employs a Dual-Phase Smoothing Strategy to ensure path smoothness. First, the anchorage area is spatially separated using a Voronoi polygon, the Risk-Aware Path Optimization Algorithm includes a grid risk function, derived from the ship domain and Gaussian influence function, in the path evaluation criteria, directing Unmanned Surface Vehicle to successfully bypass high-risk areas and as a result. Then the Dual-Phase Smoothing Strategy is used to decrease path turning points and boost path continuity, which in turn improves path economy. Simulation results demonstrate that this method significantly reduces the path length and the number of turning points, enhancing Unmanned Surface Vehicle navigation safety and economy in anchorage areas.

A sensitivity-enhanced optical fiber sensor for the detection of dissolved ammonia in water based on the Vernier effect is proposed and demonstrated. The sensor comprises a sensing Fabry-Perot interferometer (S-FPI) and a reference Fabry-Perot interferometer (R-FPI) in parallel. The S-FPI is an open cavity fabricated by the dislocation welding of single-mode fiber(SMF), which is filled with a mixture of ultraviolet-curable resin NOA 170 and Oxazine 170 perchlorate (O17). The R-FPI is a sealed cavity, which is constructed by fusion splicing of SMF and hollow core fiber (HCF). The proposed sensor is based on the chemical reaction between the dissolved ammonia and O17. This chemical reaction alters the refractive index (RI) of the S-FPI, resulting in a wavelength shift of the reflected spectrum. The two interferometers exhibit a nearly identical free spectral range (FSR), thereby enabling the generation of the Vernier effect, which markedly enhances the ammonia sensitivity of the sensor. Experimental results indicate that the sensor sensitivity is 0.34 nm/ppm in the dissolved ammonia range of 5-40 ppm, which is approximately 9.1 times that of the single S-FPI. The proposed sensor exhibits both good repeatability and a short response time, in addition to selectivity for the detection of ammonia.

A fiber-optic temperature sensor based on the harmonic Vernier effect (HVE) was proposed, which consists of a sensing Sagnac interferometer (SSI) and a reference Sagnac interferometer (RSI) in parallel. Different order HVEs are generated by controlling the length of the panda fiber in the SSI to be about an integer multiple of that in the RSI. The sensitivity and detuning of different order HVEs compared with the normal Vernier effect (NVE) are investigated, and the results show that the HVEs have almost the same temperature sensitivity as the NVE when they have the same magnification factor, while their detunings are much larger than that of the NVE, and the higher the order of the HVE, the larger the detuning. As we all know that the larger the detuning, the easier the magnification factor is to be controlled, that is, the easier the sensor is to be prepared, so from the point of view, the sensors based on the HVEs are better than those based on the NVE. This conclusion is also suitable for other fiber-optic sensors based on Vernier effect.

At present, sea ice remains a critical factor affecting the safety of vessel operations along the Northern Sea Route (NSR). However, inconsistencies between the navigability outcomes derived from the criteria for the admission of ships in the area of the Northern Sea Route (NSR criteria) and the polar operational limit assessment risk indexing system (POLARIS) methodology present challenges for navigational decision-making. This study aims to conduct a systematic comparison of the POLARIS methodology and the NSR criteria in evaluating the navigability of independently operating vessels classified as Arc4 to Arc9. Through comparative calculations of navigability and the navigability rates for six ice-class vessels across 27 districts using the two methods, this study reveals the consistencies and discrepancies in their navigability outcomes. Firstly, using the POLARIS methodology, the risk index outcome (RIO) is calculated for six ice-class vessels across 27 districts. For these districts, the navigability threshold is defined when 95% or more of the area exhibits an RIO greater than or equal to zero. Secondly, using the NSR criteria, navigability ratios for six ice-class vessels under varying ice conditions are evaluated. A navigability threshold is defined when 95% or more of the ice conditions in a district are classified as navigable. Finally, a quantitative comparison of the weekly navigability ratios obtained by the two methods is conducted to reveal the consistencies and discrepancies in the navigability outcomes of each ice-class vessel across different NSR districts. The results indicate that the consistency between the navigability outcomes of the two methods decreases with lower vessel ice classes, particularly in September and March. In general, the consistency of performance between the two methods in terms of navigability outcomes deteriorates as the vessel ice class decreases and ice conditions become more complex. This study provides a scientific foundation and data-based support for route planning and real-time decision-making in polar waters.

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images.