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Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate. [ABSTRACT FROM AUTHOR]

Roadway excavation is the leading project in coal mining, and the cantilever roadheader is the main equipment in roadway excavation. Autonomous cutting by cantilever roadheaders is the key to realize safe, efficient and intelligent tunneling for underground roadways. In this paper, the working device of a cantilever roadheader was simplified into a series of translation or rotation joints, and the spatial pose model and spatial pose coordinate system of the roadheader were established. Using the homogeneous transformation matrix and the robot-related theory, the space pose transformation matrix of the roadheader and the space pose equation of the cutting head of the roadheader were derived. The forward kinematics and inverse kinematics of the cutting head were solved by using the D-H parameter method and an inverse transformation method. The location coordinates of the inflection point of the cutting process path for a rectangular roadway were determined, and cutting path planning and control were carried out based on the inflection point coordinates. Finally, MATLAB software was used to simulate the limit cutting area of the cutting head and the cutting process path. The simulation results showed that the limit cutting section had a bulging waist shape, the boundary around the roadway was flat, and the roadway cutting error was controlled within 1mm, which verified the reliability and effectiveness of the autonomous cutting theory of the roadheader. It lays a mathematical model and theoretical foundation for the realization of "autonomous operation of unmanned tunneling equipment". [ABSTRACT FROM AUTHOR]

In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy. [ABSTRACT FROM AUTHOR]

Autonomous posture detection and self-localization of roadheaders is the key to automatic tunneling and roadheader robotization. In this paper, a multi-sensor based positioning method, involving an inertial system for altitude angles measurement, total station for coordinate measurement, and sensors for measuring the real-time length of the hydraulic cylinder is presented for roadheader position measurement and posture detection. Based on this method, a positioning model for roadheader and cutter positioning is developed. Additionally, flexible trajectory planning methods are provided for automatic cutting. Based on the positioning model and the trajectory planning methods, an automatic cutting procedure is proposed and applied in practical tunneling. The experimental results verify the high accuracy and efficiency of both the positioning method and the model. Furthermore, it is indicated that arbitrary shapes can be generated automatically and precisely according to the planned trajectory, employing the automatic cutting procedure. Therefore, unmanned tunneling can be realized by employing the proposed automatic cutting process. [ABSTRACT FROM AUTHOR]