ABSTRACT
Robotics is an overarching trend in modern high-tech production, contributing significantly to automation. They are used in various industries to perform multiple tasks, and their number is constantly growing. Robots interact with the production object with the help of gripping systems, which are an essential component of industrial robots and manipulators designed for reliable grasping. Therefore, the process of design and rational selection of grippers for considering production conditions receives considerable attention worldwide. The article offers a comprehensive approach to the design of gripper systems as an integral element of the "gripping system - part - environment - production equipment" system to ensure further rational selection considering specific production conditions. A scientific approach to assessing the design of gripping systems was proposed to systematize knowledge in designing gripping systems. In the paper, the principal structural scheme of the robotic gripping system was developed, and the purpose of elements and design requirements were determined. Also, the sequence of stages in the process of selecting the elements of the gripping system has been proposed. The comprehensive system "gripping system - part - environment - production equipment" has been identified considering the mutual influence of structural elements. This work may be helpful to engineers and researchers while designing new gripping systems or selecting the most suitable one from the database. It can improve the rational selection of the element base and the structure of the gripping system by systematizing the experience in the gripper system design. Moreover, due to modern trends in automation and digitalization, the presented classification and coding system for gripping systems can be used in Computer Aided Process Planning and Computer Aided Gripping Systems Design systems. It can help to realize the approach "from the part geometry to the gripping systems design". Also, it will ensure the production planning stage's effectiveness due to reducing the time for robotic gripping systems' design and increasing production safety, flexibility, autonomy, and performance.
ABSTRACT
Gradual development is moving from standard visual content in the form of 2D data to the area of 3D data, such as points scanned by laser sensors on various surfaces. An effort in the field of autoencoders is to reconstruct the input data based on a trained neural network. For 3D data, this task is more complicated due to the demands for more accurate point reconstruction than for standard 2D data. The main difference is in shifting from discrete values in the form of pixels to continuous values obtained by highly accurate laser sensors. This work describes the applicability of autoencoders based on 2D convolutions for 3D data reconstruction. The described work demonstrates various autoencoder architectures. The reached training accuracies are in the range from 0.9447 to 0.9807. The obtained values of the mean square error (MSE) are in the range from 0.059413 to 0.015829 mm. They are close to resolution in the Z axis of the laser sensor, which is 0.012 mm. The improvement of reconstruction abilities is reached by extracting values in the Z axis and defining nominal coordinates of points for the X and Y axes, where the structural similarity metric value is improved from 0.907864 to 0.993680 for validation data.
ABSTRACT
Nowadays, artificial intelligence is used everywhere in the world and is becoming a key factor for innovation and progress in many areas of human life. From medicine to industry to consumer electronics, its influence is ever-expanding and permeates all aspects of our modern society. This article presents the use of artificial intelligence (prediction) for the control of three motors used for effector control in a spherical parallel kinematic structure of a designed device. The kinematic model used was the "Agile eye" which can achieve high dynamics and has three degrees of freedom. A prototype of this device was designed and built, on which experiments were carried out in the framework of motor control. As the prototype was created through the means of the available equipment (3D printing and lathe), the clearances of the kinematic mechanism were made and then calibrated through prediction. The paper also presents a method for motor control calibration. On the one hand, using AI is an efficient way to achieve higher precision in positioning the optical axis of the effector. On the other hand, such calibration would be rendered unnecessary if the clearances and inaccuracies in the mechanism could be eliminated mechanically. The device was designed with imperfections such as clearances in mind so the effectiveness of the calibration could be tested and evaluated. The resulting control of the achieved movements of the axis of the device (effector) took place when obtaining the exact location of the tracked point. There are several methods for controlling the motors of mechatronic devices (e.g., Matlab-Simscape). This paper presents an experiment performed to verify the possibility of controlling the kinematic mechanism through neural networks and eliminating inaccuracies caused by imprecisely produced mechanical parts.
Subject(s)
Artificial Intelligence , Neural Networks, Computer , Humans , Electronics , Movement , KineticsABSTRACT
Inspection systems are currently an evolving field in the industry. The main goal is to provide a picture of the quality of intermediates and products in the production process. The most widespread sensory system is camera equipment. This article describes the implementation of camera devices for checking the location of the upper on the shoe last. The next part of the article deals with the analysis of the application of laser sensors in this task. The results point to the clear advantages of laser sensors in the inspection task of placing the uppers on the shoe's last. The proposed method defined the resolution of laser scanners according to the type of scanned surface, where the resolution of point cloud ranged from 0.16 to 0.5 mm per point based on equations representing specific points approximated to polynomial regression in specific places, which are defined in this article. Next, two inspection systems were described, where one included further development in the field of automation and Industry 4.0 and with a high perspective of development into the future. The main aim of this work is to conduct analyses of sensory systems for inspection systems and their possibilities for further work mainly based on the resolution and quality of obtained data. For instance, dependency on scanning complex surfaces and the achieved resolution of scanned surfaces.
