Industrial manipulator dynamic parameter estimation using mutating particle swarm optimization (Mupso)

This work aims at developing a dynamic model and estimating the unknown parameters of the first three joints (at the arm) of a 6 degree of freedom industrial robot manipulator, a finite Fourier series algorithm was used to design an excitation trajectory, a mutating particle swarm optimization algorithm was used to optimise the parameters of the Fourier series thereby minimizing the condition number of the observation matrix, and a linear least-squares methods was implemented for estimating the unknown dynamic parameters of the manipulator. A mutation function was implemented to break the algorithm out of stagnation. Out of the thirty unknown parameters at the industrial manipulator arm, twenty were identified independently, two were identifiable in linear combinations, and the remaining eight parameters were unidentifiable. The mutating particle swarm optimization algorithm dominated other algorithms and was found suitable for robot dynamic analysis.

MP-9S Industrial Manipulator Handling and Scrapping Tasks in Industrial Environments

The main goal of the project was to carry out a handling task using a color sensor and thermal sensor, as well as an industrial robotic unit. The selection by color made it possible to represent the sub-process of the production of a sample, where each color can be matched to the corresponding or rejected product. The function of the thermal sensor is to show the delay function for the pro-cess, which occurs when the product does not reach a point in the process at the wrong tempera-ture and needs cooling between the two workflows. The thermal sensor and color sensor were pro-grammed using the ATmega microprocessor, while the manipulator was controlled with industrial PLC. The completed project will serve educational purposes in training for students.

PID AND LQR CONTROLLERS APPLIED TO THE INVERSE DYNAMICS OF A 3-DOF MANIPULATOR

The application in the industrial manipulator robots has grown over the years making production systems increasingly efficient. Within this context, the need for efficient controllers is required to perform the control of these manipulators. In this work the PID controller (Proportional-Integral-Derivative) and LQR (Linear Quadratic Regulator) is presented from the inverse dynamics model of a RPP (Rotational - Prismatic - Prismatic) cylindrical manipulator. The inverse dynamic model which is modeled on Simulink together with a cascaded PID controller is presented. The PID and LQR results are also presented for joint independent and joint dependent control, i.e a controlled PID is used for each joint, controlling the trajectories and speeds at the same time. This paper has as main contributions the development of the manipulator dynamics model and the design of the LQR and PID controllers applied to the inverse dynamics model, which makes the system simpler to control.

A Robotic grinding station based on an industrial manipulator and vision system

Due to ever increasing precision and automation demands in robotic grinding, the automatic and robust robotic grinding workstation has become a research hot-spot. This work proposes a grinding workstation constituting of machine vision and an industrial manipulator to solve the difficulty of positioning rough metal cast objects and automatic grinding. Faced with the complex characteristics of industrial environment, such as weak contrast, light nonuniformity and scarcity, a coarse-to-fine two-step localization strategy was used for obtaining the object position. The deep neural network and template matching method were employed for determining the object position precisely in the presence of ambient light. Subsequently, edge extraction and contour fitting techniques were used to measure the position of the contour of the object and to locate the main burr on its surface after eliminating the influence of burr. The grid method was employed for detecting the main burrs, and the offline grinding trajectory of the industrial manipulator was planned with the guidance of the coordinate transformation method. The system greatly improves the automaticity through the entire process of loading, grinding and unloading. It can determine the object position and target the robotic grinding trajectory by the shape of the burr on the surface of an object. The measurements indicate that this system can work stably and efficiently, and the experimental results demonstrate the high accuracy and high efficiency of the proposed method. Meanwhile, it could well overcome the influence of the materials of grinding work pieces, scratch and rust.

Dynamic and Friction Parameters of an Industrial Robot: Identification, Comparison and Repetitiveness Analysis

This paper describes the results of dynamic tests performed to study the robustness of a dynamics model of an industrial manipulator. The tests show that the joint friction changes during the robot operation. The variation can be identified in a double exponential law and thus the variation can be predicted. The variation is due to the heat generated by the friction. A model is used to estimate the temperature and related friction variation. Experimental data collected on two robots EFORT ER3A-C60 are presented and discussed. Repetitive tests performed on different days showed that the inertial and friction parameters can be robustly estimated and that the value of the measured joint friction can be used to estimate the unexpected conditions of the joints. Future applications may include sensorless identification of collisions, predictive maintenance programs, or human–robot interaction.