Aiming at the problems of low harvesting efficiency and high risk of tree damage caused by the fixed excitation force of the traditional walnut harvester's vibration mechanism and its difficulty in adapting to different diameter grades of fruit trees, a vibration mechanism with controllable excitation force was designed. This mechanism adopted two motors to independently drive two sets of fan-shaped eccentric blocks in combination, generating three adjustable theoretical excitation forces. The excitation force was dynamically matched with the tree trunk diameter through real-time detection by a depth camera. A three-dimensional model of the vibration mechanism was established in SolidWorks, and the eccentric blocks were designed through parametric calculation. The performance of the mechanism was verified by finite element analysis and the automatic dynamic analysis of mechanical systems (ADAMS). The tree trunk diameter was identified by combining the point cloud data of the depth camera with the least squares cylindrical fitting algorithm based on random sample consensus (RANSAC). The results showed that the actual excitation forces of the three gears obtained by ADAMS software simulation were 12.40, 18.82, and 31.22 kN, with a relative error of no more than 0.80% compared with the theoretical values. ANSYS (analysis system) software analysis of the eccentric blocks showed that the modal frequency range was 755.36 to 3,983.60 Hz, effectively avoiding the working frequency of 16 Hz. The relative error tail mean of the tree trunk diameter identification algorithm verified in the field was 1.63%. The field simulation system realized the closed-loop control of “diameter grade-excitation force-amplitude”. This vibration mechanism can accurately generate three target excitation forces, has a reliable structure, and can adaptively adjust the excitation force through the input of tree trunk diameter, providing a new idea for the intelligent upgrade of forest fruit harvesting equipment.
根据前面研究的内容,基于PyCharm Community平台,开发了核桃树干直径识别算法的验证程序,并采用Intel RealSense D435i深度相机开展了田间核桃树干直径估算试验。共对10棵样本树进行了测量,具体数据见表3。试验过程中,首先获取了树干的RGB图像及原始点云数据,如图10(a)和图10(b)所示;随后通过距离滤波、体素下采样、半径离群点去除以及基于密度的空间聚类应用与噪声(density-based spatial clustering of applications with noise,DBSCAN)聚类等一系列点云预处理操作,得到滤波后的点云图像,如图10(c)所示;进而基于最小二乘圆柱拟合算法对预处理后的点云进行圆柱拟合,得到拟合效果图,如图10(d)所示,并输出树干直径估计值。为降低测量偏差,每棵树的直径估算值取3个不同视角下采集结果的平均值。最终,将算法估计值与实际测量值进行对比,得出相对误差的切尾均值为1.63%。
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