Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs.

Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework's effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.

The contribution of ocean-based solutions to carbon reduction in China.

Climate change has become a great challenge for humanity. However, the current climate change mitigation measures, primarily concentrate on land, more or less neglecting the vital role of the ocean-based solutions. While the ocean is a crucial regulator of the global climate, ocean-based solutions could also play an essential role in climate change mitigation and policymaking. This paper developed an Ocean-based Solutions Carbon Reduction Assessment Model (OSCRAM) that addresses coastal ecosystems, ocean energy, marine transportation, fishery, and seabed to estimate the oceanic contribution to climate change mitigation. It has been applied to evaluate the capacity of carbon emission reduction through oceans in China. We found that the total contribution for carbon emission reduction was about 6.86 Tg CO2 per year, and it may reach 139.39 Tg in 2030 under the target scenario. The results indicated that the ocean has huge potential to reduce carbon emissions. The development of marine energy and low-carbon marine shipping may have more potential for emission reduction in China, and the government should also protect and restore coastal wetlands for their enormous carbon storage. It can also provide a reference for the globe and other nations in achieving emission reduction goals.

A curve evolution approach for unsupervised segmentation of images with low depth of field.

In this paper, we describe a novel algorithm for unsupervised segmentation of images with low depth of field (DOF). First of all, a multi-scale reblurring model is used to detect the object of interest (OOI) in saliency space. Then, to determine the boundary of OOI, an active contour model based on hybrid energy function is proposed. In this model, a global energy item related with the saliency map is adopted to find the global minimum, and a local energy term regarding the low DOF image is used to improve the segmentation precision. In addition, an adaptive parameter is attached to this model to balance the weight of global and local energy. Furthermore, an unsupervised curve initialization method is designed to reduce the number of evolution iterations. Finally, we conduct experiments on various low DOF images, and the results demonstrate the high robustness and precision of the proposed approach.