intelligent systems used in intelligent coal mines [4; 5]. _Made in China 2025--Energy Equipment Implementation Plan_ (Fa Gai Energy [2016] No. 1274) clearly requires the development of intelligent washing equipment, focusing on the development of 10 million tons/year of modular intelligent washing equipment and an intelligent control system. Based on information sensing, artificial intelligence, video monitoring, robots, and other technologies, the automatic and less humanized control of the coal washing process represents the developmental direction of intelligent washing systems. In this paper, a comprehensive calibration method is proposed to avoid the influence of robot installation error on grasping accuracy, and a duplicate image elimination calculation method is proposed to solve the problem of duplicate collection and missed shooting of gangue images. Coal gangue washing can be divided into manual and mechanical separation. With the rapid development of modern robot technology, using robots with vision systems to complete highly repetitive sorting work instead of manual work is becoming an increasingly prevalent trend [6; 7]. The traditional coal gangue automatic separation method based on image recognition or ray identification mainly adopts high-pressure air gun jet impact, which has low efficiency and high noise. References [8; 9; 10] studied the coal gangue automatic sorting system and sorting robot with different structural forms, and references [11; 12; 13; 14; 15] studied the coal gangue image recognition technology. These research results promoted the construction process of intelligent coal mine washing, and also proved that coal gangue sorting robots can automatically identify and sort coal gangue, thereby greatly improving the quality and efficiency of coal gangue sorting and greatly benefiting both the economy and society. Delta parallel robots are widely used in packaging and sorting, assembly and painting, transportation, and palletizing due to its advantages of high speed, strong bearing capacity, and small cumulative error. Parallel robots with this structure can be used in the field of coal gangue online identification and sorting through the configuration of visual recognition systems. The integrated calibration between the vision system, the robot, and the conveyor belt is the basis for robots achieving high-precision grasping control. Due to the influence of uncontrollable factors such as machining error and installation error, errors are present in the ideal structural parameters, such as hinge position and rod length, compared with the actual structural parameters, thereby making the kinematic model of the parallel robot inaccurate and affecting the accuracy of the parallel robot [16; 17]. References [18; 19] studied the accuracy calibration of Delta parallel robots, whereas reference [20] presented a two-stage calibration method for the parallel robot Delta 4. Reference [21] focused on the problem of the initial pose estimation by means of proprioceptive sensors (self-calibration) of suspended, underactuated, cable-driven, parallel robots. References [22; 23; 24; 25] studied the calibration technology of robot kinematics, and references [26; 27; 28; 29; 30] studied robot vision calibration technologies. The above methods are suitable for high-precision positioning occasions, but are not suitable for establishing accurate kinematic models, and the calibration procedures are complex. On this basis, the traditional conveyor belt and vision calibration method of coal gangue sorting parallel robot can be improved; a comprehensive calibration method is therefore proposed to avoid the influence of robot installation error on grasping accuracy. The main innovations of this comprehensive calibration are as follows. Using this conveyor belt calibration method, the accurate positions of each coordinate in the robot coordinate system can be determined so that the end of the robot arm can accurately grasp objects on the conveyor belt.