scholarly journals Training Robot Arm 5 Degree of Freedom for Tracking the desired route using MLP

2017 ◽  
Vol 2 (3) ◽  
pp. 232-239
Author(s):  
Zahed Kamangar ◽  
Soran Saeed ◽  
Asrin Zardoie
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Vision System ◽  
Industrial Robot ◽  
Robot Arm ◽  
Static State ◽  
Voltage Range ◽  
End Point ◽  
Working Space ◽  
Neural Network Input

This paper work presents a new method of controlling the robot arm. The control system is the most important part of industrial robot. In industrial robot arms, it is very important to control the desired path and direction. In this paper, the presented control method is a multilayer neural network. Which controls and compares the location of the joins at the end point of the path relative to the zero position (the beginning of the path-static state). And try to learn the ultimate position of each joints due to changes in angles and direction of movement to carry out the motion process. The superiority of this method is that it can operate without considering 3D space (working space), the dynamic equations, and have Cartesian coordinates of the points on the desired path. Innovating this method of controlling the choice of the route is based on feedback from the vision system and human intelligence. This way, the operator selects and applies how to move the joints and the links of the robot and the method of walking the path. Applying the path through the movement of links and motion of joints and changing their angles in order to reach the end effector to the end point of the path. In this system, using the potentiometers (volumes) as an encoder connected to the axis of the joints, it is possible to obtain the location of the joints on the basis of variations in the voltage range and convert it to the equivalent digital 1024-0 values as has been used the MLP neural network input.

scholarly journals Detection Method of Welding Defects in a Robotic Station Using the Deep Neural Network

2021 ◽  
Vol 25 (1) ◽  
pp. 67-72
Author(s):  
Arkadiusz Adamczak
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Weld Seam ◽  
Detection Method ◽  
Vision System ◽  
Industrial Robot ◽  
Welding Defects ◽  
Basic Quality

Modern automation and robotization of production processes requires new and fast methods of product quality control. In the case of arc welding in robotic systems, where the production process takes place in large series, it is important to quickly control the correctness of the weld. Based on visual data, the system should be able to automatically determine whether a given weld meets the basic quality requirements, and thus be able to stop the process in the event of identified defects. The article presents the results of research on the creation of a visual method for assessing the correctness of the weld seam based on the deep neural network classifying, locating and segmenting welding defects. The proposed detection method was extended by using a combination of a vision system camera with a six-axis industrial robot in order to enable detection of a larger number of welding defects and positioning in a six-dimensional workspace. The research results presented in this article were obtained during the implementation of the project entitled „Development of a method based on the use of deep neural networks for visual inspection of welded joints in the course of R&D works” implemented at the company ZAP-Robotyka Sp. z o.o. in Ostrów Wielkopolski.

scholarly journals Accuracy Improvement of Robot Arm Trajectory based on Adaptive Neural Network Algorithm

CONVERTER ◽  
2021 ◽  
pp. 709-715
Author(s):  
Peibo Li, Peixing Li, Chen Yanpeng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Tracking Error ◽  
Network Control ◽  
Neural Network Control ◽  
Robot Arm ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Simulation Results

An adaptive neural network control method was proposed to solve the problems such as unstable motion and large trajectory tracking error when the robot arm was disturbed by the external environment.The dynamic equations of the manipulator were given and the dynamic characteristics of the manipulator were studied by using the positive feedback neural network. Then the adaptive neural network control system was designed, and the stability and convergence of the closed-loop system were proved by the Lyapunov function. Later, the model diagram of the robot arm was established, and the dynamics parameters of the manipulator were simulated by MATLAB /Simulink software.At the same time, they were compared with the simulation results of the PID control system for analysis.The simulation results showed that the trajectory tracking error and input torque fluctuation were smaller when the trajectory of the robot arm was disturbed by the external world. When adopting the control method of the adaptive neural network, the robot arm could improve the control precision of the trajectory, thus reducing the jitter of the robot arm motion.

Design and performance analysis of an industrial robot arm for robotic drilling process

2019 ◽  
Vol 46 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Longfei Sun ◽  
Fengyong Liang ◽  
Lijin Fang
Keyword(s):  
Control Method ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Ball Screw ◽  
Robotic Arm ◽  
Drilling Process ◽  
Robot Arm ◽  
Content Type ◽  
Robotic Drilling

Purpose The purpose of this paper is to present a robotic arm that can offer better stiffness than traditional industrial robots for improving the quality of holes in robotic drilling process. Design/methodology/approach The paper introduces a five-degree of freedom (DOF) robot, which consists of a waist, a big arm, a small arm and a wrist. The robotic wrist is composed of two DOFs of pitching and tilting. A parallelogram frame is used for robotic arms, and the arm is driven by a linear electric cylinder in the diagonal direction. Double screw nuts with preload are used in the ball screw to remove the reverse backlash. In addition, dual-motor drive is applied for each DOF in the waist and the wrist to apply anti-backlash control method for eliminating gear backlash. Findings The proposed robotic arm has the potential for improving robot stiffness because of its truss structure. The robot can offer better stiffness than industrial robots, which is beneficial to improve the quality of robotic drilling holes. Originality/value This paper includes the design of a five-DOF robot for robotic drilling tasks, and the stiffness modeling of the robot is presented and verified by the experiment. The robotic system can be used instead of traditional industrial robots for improving the hole quality to a certain extent.

Recognition of Partially Occluded Objects with Back-Propagation Neural Network

1998 ◽  
Vol 12 (05) ◽  
pp. 645-660 ◽  
Author(s):  
Geok See Ng ◽  
Hak Chuah Sim
Keyword(s):  
Vision System ◽  
Industrial Robot ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Robot Arm ◽  
Occluded Objects ◽  
Fast Recognition ◽  
Automatic Picking ◽  
Partially Occluded ◽  
The Military

The problem of occlusion in a two-dimensional scene introduces errors into many existing vision algorithms that cannot be resolved. Occlusion occurs where two or more objects in a given image touch or overlap one another. Since occlusion will be present in all but the most constrained environment, the recognition of partially occluded objects is important for industrial machine vision applications to solve real problems in the military domain and in factory automation. A new method is proposed in this paper to identify and locate objects lying on a flat surface. The method is based on a local and compact description of the objects' boundaries and a new fast recognition method involving neural networks. The merit of such approach is that it provides strong robustness for partially occluded object recognition. The method is integrated into a vision system that couples with an industrial robot arm to provide automatic picking and repositioning of partially occluded industrial parts.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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