Path planning and image classification algorithms for glass cleaning robots based on hybrid path planning and convolutional neural networks

With the development of the times and the progress of society, the number of high-rise buildings is increasing, and the cleaning of the outer glass at height is extremely difficult, relying on manual cleaning with high safety risks and low efficiency. The trend is to use automated control products such as cleaning robots to take on some of the difficult cleaning work. Path planning and image recognition for glass cleaning robots is a hot issue for cleaning robots to achieve efficient cleaning. In this paper, a path planning and image recognition method for glass cleaning robots based on hybrid path planning and convolutional neural networks is proposed to address this hot problem. The cleaning robot adopts a hybrid path of contour-parallel path and method-parallel path to achieve full-coverage path planning, and uses a portable camera to take photos of the glass, and uses a convolutional neural network model to determine the degree of dirty glass, and uses this as the basis for different cleaning processes, effectively improving the efficiency of cleaning and the utilisation of resources. In this paper, we design an intelligent control algorithm for a glass cleaning robot that combines artificial intelligence and 3D printing path planning generation by combining two of these methods, which helps to improve the cleaning effect of the cleaning robot.