Wall-Climbing Robot for ShipCleanin

With the development of the global economy and the advancement of China's "Maritime Power" strategy, the ocean shipping industry has grown rapidly. However, the fouling on ship surfaces caused by seawater corrosion and marine organism attachment significantly increases fuel consumption and operational costs. To address this, this study combines cavitation jet technology, magnetic adsorption track structure, and a convolutional neural network (CNN) image recognition system to develop a new type of wall-climbing ship cleaning robot. This robot achieves precise positioning and efficient cleaning of hull surface attachments. Experiments show that its cleaning efficiency reaches 95 m²/h, a 300% improvement over traditional manual cleaning, and it can reduce fuel consumption by 12% to 15%, thereby significantly lowering greenhouse gas emissions. Therefore, this study provides an environmentally friendly and efficient solution, helping to optimize ship maintenance processes and enhance economic benefits.