This paper presents a rigid-flexible composite of bionic hand structure design scheme solution for solving the problem of low load on the soft gripping hand. The bionic hand was designed based on the Fast Pneumatic Network (FPN) approach, which can produce a soft finger bending drive mechanism. A soft finger bending driver was developed and assembled into a human-like soft gripping hand which includes a thumb for omnidirectional movement and four modular soft fingers. An experimental comparison of silicone rubber materials with different properties was conducted to determine suitable materials. The combination of 3D printing technology and mold pouring technology was adopted to complete the prototype preparation of the bionic hand. Based on the second-order Yeoh model, a soft bionic finger mathematical model was established, and ABAQUS simulation analysis software was used for correction to verify the feasibility of the soft finger bending. We adopted a pneumatic control scheme based on a motor micro-pump and developed a human–computer interface through LabView. A comparative experiment was carried out on the bending performance of the finger, and the experimental data were analyzed to verify the accuracy of the mathematical model and simulation. In this study, the control system was designed, and the human-like finger gesture and grasping experiments were carried out.

In view of the phenomenon that camera imaging will appear large up close and small from afar in the eye-to-hand hand-calibration system, one hand–eye calibration is carried out. The manipulator is only suitable for grasping objects of the same height, and the calibration results cannot be applied to grasping products with variable height. Based on the study of the pinhole camera model and the rigid transformation model between coordinate systems, the introduction of the calibration height parameters, the relationship between parameters of the rigid transformation matrix between image the coordinate system and the robot coordinate system, and sampling height are established. In the experiment, firstly, through the calibration of camera parameters, the influence of camera distortion on imaging quality is eliminated, and the influence of calibration height is ignored. Then, the machine coordinate system and image coordinate system of the calibration plate at different heights are calibrated using the four-point calibration method. The parameters of the rigid transformation matrix at different heights (H) are calculated. Finally, through experimental analysis, the high linear relationship between the parameters of the rigid transformation matrix from the image coordinate system to the robot coordinate system and the calibration height is fitted. By analyzing the random error of the experiment, the linear relationship between calibration height and pixel density is further established, and the systematic error of the experimental process is deeply analyzed. The experimental results show that the hand–eye calibration system based on this linear relationship is precise and suitable for grabbing products of any height, and the positioning error is less than 0.08%.