# Navigation Path Extraction and Experimental Research of Pusher Robot Based on Binocular Vision
On the other hand, under the mature application of image processing, embedded, electromechanical control and other technologies, visual signals stand out due to their advantages of a large amount of information and strong applicability [9; 10]. Machine vision has become an important direction for the development of autonomous robot operation technology. Agricultural robots such as pusher robots have also begun to develop in the direction of intelligence, becoming a research hotspot in self-propelled robots and precision animal husbandry. In recent years, researchers at home and abroad have carried out related studies on the extraction of navigation paths for agricultural robots based on vision. [PERSON] et al. proposed a new algorithm for the fitting of a navigation path for greenhouse cucumber-picking robots. The proposed prediction point, the Hough transform, fits the navigation paths with an average error less than 0.5\({}^{\circ}\), which is 10.25\({}^{\circ}\) lower than the average error of the least-square method [11]. [PERSON] et al. proposed a novel path detection approach for orchards using CNN-based partial area classification. The approach involves generating a path score map by using a four-layer CNN for tree and path classification, path area segmentation and target path detection by using boundary line determination [12]. Whether in a greenhouse sprayer [13], rubber-tapping robot [14] or tomato harvesting robot [15], machine vision was used in navigation path extraction and tracking control research. The research and development of various agricultural robots with autonomous navigation and obstacle avoidance functions can complete various automated operations, improve the operational efficiency of agricultural labor and reduce overall production costs. Therefore, in this study, the visual navigation method will be applied to develop a pusher robot for dairy farms. At present, the existing research at home and abroad has solved the problem of navigation path extraction in some agricultural scenarios, but the working environment of dairy farms is rarely mentioned. In this study, a new type of machine vision system was developed to fill this gap. The system will be used for the extraction and tracking control of the working path of the pusher robot. The binocular vision sensor was used as the image acquisition device, and only binary images were used in the algorithm, thus reducing the computational load of the system. A method for extracting the working path of a pusher robot based on binocular vision proposed in this study is summarized as follows: Firstly, the camera mounted on the robot acquired the image of the working area in front of the robot in real time and preprocessed the image by means of sky removal, noise removal and grayscale. Then, the preprocessed image was segmented, and, at the same time, the front fence, feed belt and ground data were collected. Finally, the navigation path was obtained by extracting the characteristics of the feed belt. The system designed in this study could autonomously generate accurate navigation paths for robots in a dynamic farm environment, which will provide a technical reference for the autonomous navigation of farming robots and the development of precision animal husbandry. This paper is organized as follows: Section 2 details the materials and methods employed to achieve the research objective. In Section 3, the experimental results and discussion on the proposed technique are presented. Finally, in Section 4, the conclusion and future work is provided.