In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary.

In this paper, an adaptive fuzzy tracking control method is proposed to address the issues of dead-zone and unobservable states in a flexible robotic arm system. The control design process begins with the utilization of a fuzzy logic system to approximate the nonlinear functions present in the flexible robotic arm system. To estimate the unobservable states of the system, a state observer is then designed. To alleviate the computational complexity during controller design, the command filtering technique is introduced. Additionally, the Nussbaum function is incorporated to address the unknown control gain problem. The stability of the system can be verified through the design of a Lyapunov function. This study’s simulation results demonstrate that the designed control system can closely track the specified reference signals. The closed-loop system effectively controls the flexible robotic arm, as verified through experimentation.