Optimization of Sorting Robot Control System Based on Deep Learning and Machine Vision

Abstract

To enhance the control technology of coal gangue dry separation method which is replaced by the machine in coal washing plant and to explore the control effects of traditional PID and dynamic domain fuzzy self-tuning PID, which will aid in determining the ideal position and orientation for grasping an object as well as understanding physical and logistic data patterns, an optimal design of PID controller for sorting robot based on deep learning is initiated. The mathematical model of ball screw system driven by a single joint motor of the robot is introduced, the control effects of classical PID and variable domain fuzzy self-tuning PID are studied and imitated, respectively. The simulation outcome appears that the selection time is 0.001 s and simulation time is 8 s. The tracking error of variable domain fuzzy PID is minor than that of PID tracking at the starting point, and the convergence rate of error is quick than that of PID manage, the steady-state error is minor than PID, the control accuracy is higher, and the tracking performance is better. The advantages of variable domain fuzzy PID control method in position tracking control are verified, the variable domain fuzzy PID can modify the control framework online as per the different position mistake and mistake change rate, the design of the variable domain of input and output makes the fuzzy inference rules locally finer, the speed of adjustment is faster and the tracking accuracy is further improved, so it has finer tracking presentation than the traditional PID tracking management.

Construction and Simulation of Deep Learning Algorithm for Robot Vision Tracking

“…Therefore, the motion model can be used to generate candidates around the location of the object in a frame. At present, there are mainly two ways to generate candidate frames: particle filter and sliding window particle filter, which use the predicted position of the above frame as the center to transform the radiation parameters of six candidate frames, so as to obtain a series of candidate samples [8]. The six parameters include horizontal displacement, vertical displacement, rotation angle, aspect ratio, stretch ratio, and dimension particle filter.…”

Pantograph Catenary Performance Detection of Energy High-Speed Train Based on Machine Vision

“…During the test, the transverse motion range of the reciprocating device is concentrated near the center of the carbon sliding plate [25]. In order to better compare the test results with other methods, select the test results of a certain test under the condition of static debugging, use the monocular CCD camera [26][27][28][29] to detect the pull-out value, and analyze the accuracy of the machine vision system [30][31][32][33] by comparing the relative errors between them. e comparison results are shown in Table 2.…”

Artificial intelligence for waste management in smart cities: a review

“…Detect recyclables 3. Generate a three-dimensional circumferential map of robot positioning, navigation, and path planning The applicability of recycling robots has been expanded, and the robustness has been improved Feng et al ( 2022 ); Wang et al ( 2020 ) Deep learning simultaneous localization and mapping Map reconstruction, navigation, repositioning, waste detection, and sequencing The error difference is small Bobulski and Kubanek ( 2021b ); Chang et al ( 2020 ); Chen et al ( 2022b ); Zhou et al ( 2021 ) Computer vision Hyperspectral image 1. Adjust the gripper 2.…”