Компенсація похибок позиціонування роботаманіпулятора в робочому просторі технологічного обладнання

Промислові роботи широко використовують на сучасних підприємствах. Хоча висока повторюваність промислових роботів може задовольнити більшість потреб процесу, їх низька абсолютна точність позиціонування в робочому просторі не може задовольнити вимоги деяких високоточних завдань. Прикладом є те, що дія робота покладається на вибіркові дані в реальному часі, а не на змодельовані дані. Низька абсолютна точність робота до позиціонування в робочому просторі обладнання призводить до того, що робоча продуктивність значно відрізняється від очікуваної. Тож метою даної статті є аналіз розробленої програми підвищення точності позиціонування робота-маніпулятора в реальному часі за умови компіляції з програмами інтеграційних проєктів автоматизації. У статті розглянуто деформацію ланок, що є однією з виробничих похибок робота-маніпулятора. Дія прикладених зусиль на з'єднаннях і ланках може по-різному впливати на деформацію пози робота, а ці зміни складно врахувати за допомогою лінійної алгебри, тому останнім часом зріс попит на "офлайнове програмування", що дозволить змоделювати роботу робота-маніпулятора зі зміною у часі і матиме прийнятну ціну. Тому було розглянуто застосування штучних нейронних мереж на основі алгоритмів оптимізації для компенсації абсолютної похибки. Після аналізу існуючих технічних рішень було обрано генетичний алгоритм, що задовольняє умови простоти реалізації, зрозумілості, відсутність зайвих обчислень, гнучкість до використовуваних типів даних. Також в роботі представлено загальну схему алгоритмів оптимізації параметрів штучних нейронних мереж та ітеративний процес пошуку оптимальних значень гіперпараметрів із використанням генетичного алгоритму. Після вибору методу моделювання та алгоритму оптимізації, протестовано розроблений алгоритм на відкритому наборі даних. Отримані результати показали підвищення точності від 22 до 77 % (точності позиціонування без застосування компенсаційної методики). Отже, в даній статті описано метод підвищення абсолютної точності позиціонування за умови компіляції з програмами інтеграційних проєктів автоматизації. Результатом роботи є програма, що заснована на вищезгаданому методі.Industrial works are widely used in modern enterprises. Although high repeatability of industrial robots can satisfy most process needs, their low absolute positioning accuracy in the workspace cannot meet the requirements of some high-precision tasks. An example, is that the action of a robot relies on real-time sample data rather than simulated data. The low absolute robot’s accuracy to positioning leads to the fact that work productivity is significantly different from what is expected. Therefore, the purpose of this paper is to analyze the developed program to increase the accuracy of robot-manipulator’s positioning in real time under the compilation with the programs of integration automation projects. The paper considers the deformation of the links, which is one of the robot manipulator’s production errors. The effect of applied forces on the connections and links can affect the deformation of the robot posture in different ways, and these changes are difficult to account for using linear algebra, so recently the demand for "offline programming" has increased, which will allow you to simulate the work of a robot manipulator with a change in time and will have a reasonable price. Therefore, the application of artificial neural networks based on optimization algorithms to compensate for absolute error was considered. After analyzing the existing technical solutions, a genetic algorithm was chosen that satisfies the conditions of ease of implementation, clarity, lack of redundant calculations, flexibility to the data types used. The paper also presents a general scheme of algorithms for optimizing the parameters of artificial neural networks and an iterative process of finding optimal values of hyperparameters using a genetic algorithm. After choosing the modelling method and optimization algorithm, the developed algorithm on an open data set was tested. The obtained results showed an increase in accuracy from 22 to 77% (positioning accuracy without the use of compensation methods). Therefore, this article describes a method of increasing the absolute accuracy of positioning under the condition of compilation with programs of integration automation projects. The result is a program based on the above method

Design of Object Identification System Based on Machine Vision

Object sorting is an important aspect in almost all the industries. Production industries like food, chemical, petroleum and textile industries have to sort objects on numerous parameters. Various automated object sorting systems are required to avoid human flaws, with increase in productivity and reduce the overall time. Objective of the present work is to develop a part identification system using machine vision. Due to the advantage of LabVIEW in controlling hardware effectively it is employed in the present work. The Vision camera once identifies an object based on its attributes like color shape and size, immediately a signal should be communicated with the controller for separating that object. In this work the signal is shown as a glowing LED. Also the number of objects of particular category passing on the conveyor is counted and displayed to illustrate moving objects identification. A low speed conveyor belt is fabricated with different test objects passing over it. For identifying colors, wavelength data is used, for identifying the shape geometric pattern matching is used and for identifying the size edge detection is applied. The developed G-programming environment generates a graphic user interface in front panel. Ability to count the objects of specific attribute is tested for different trail runs. Thesis is organized as follows: Chapter 1 contains introduction to machine vision system, its components, the objectives of present study and literature review of similar works. Chapter 2 deals with various methods used in vision and their implementation in LabVIEW as done in this work was presented in chapter 3. Chapter 4 gives brief conclusions and future scope of present work

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

Unexpected equipment downtime is a ‘pain point’ for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system

AN ALGORITHM FOR RECONSTRUCTING THREE-DIMENSIONAL IMAGES FROM OVERLAPPING TWO-DIMENSIONAL INTENSITY MEASUREMENTS WITH RELAXED CAMERA POSITIONING REQUIREMENTS, WITH APPLICATION TO ADDITIVE MANUFACTURING

Cameras are everywhere for security purposes and there are often many cameras installed close to each other to cover areas of interest, such as airport passenger terminals. These systems are often designed to have overlapping fields of view to provide different aspects of the scene to review when, for example, law enforcement issues arise. However, these cameras are rarely, if ever positioned in a way that would be conducive to conventional stereo image processing. To address this, issue an algorithm was developed to rectify images measured under such conditions, and then perform stereo image reconstruction. The initial experiments described here were set up using two scientific cameras to capture overlapping images in various cameras positons. The results showed that the algorithm was accurately reconstructing the three-dimensional (3-D) surface locations of the input objects. During the research an opportunity arose to further develop and test the algorithms for the problem of monitoring the fabrication process inside a 3-D printer. The geometry of 3-D printers prevents the location of cameras in the conventional stereo imaging geometry, making the algorithms described above seem like an attractive solution to this problem. The emphasis in 3-D printing on using extremely low cost components and open source software, and the need to develop the means of comparing observed progress in the fabrication process to a model of the device being fabricated posed additional development challenges. Inside the 3-D printer the algorithm was applied using two scientific cameras to detect the errors during the printing of the low-cost open-source RepRap style 3-D printer developed by the Michigan Tech’s Open Sustainability Technology Lab. An algorithm to detect errors in the shape of a device being fabricated using only one camera was also developed. The results show that a 3-D reconstruction algorithm can be used to accurately detect the 3-D printing errors. The initial development of the algorithm was in MATLAB. The cost of the MATLAB software might prevent it from being used by open-source communities. Thus, the algorithm was ported to Python and made open-source for everyone to use and customize. To reduce the cost, the commonly used and widely available inexpensive webcams were also used instead of the expensive scientific cameras. In order to detect errors around the printed part, six webcams were used, so there were 3 pairs of webcams and each pair were 120 degrees apart. The results indicated that the algorithms are precisely detect the 3-D printing errors around the printed part in shape and size aspects. With this low-cost and open-source approach, the algorithms are ready for wide range of use and applications