Web Service for Point Cloud Supported Robot Programming Using Machine Learning

Abstract

In industrial automation, the use of robots is already standard. But there is still a lot of room for further automation. One such place where improvements can be made is in the adjustment of a production system to new and unknown products. Currently, this task includes the reprogramming of the robot and a readjustment of the image processing algorithms if sensors are involved. This takes time, effort, and a specialist, something especially small and middle-sized companies shy away from. We propose to represent a physical production line with a digital twin, using the simulated production system to generate labeled data to be used for training in a deep learning component. An artificial neural network will be trained to both recognize and localize the observed products. This allows the production line to handle both known and unknown products more flexible. The deep learning component itself is located in a cloud and can be accessed through a web service, allowing any member of the staff to initiate the training, regardless of their programming skills. In summary, our approach addresses not only further automation in manufacturing but also the use of synthesized data for deep learning.

Щодо Аналізу Стратегій Підвищення Ефективності Виробництв За Рахунок Інтеграції Мобільних Роботів

“…Машинне навчання є іншою ключовою стратегією, яка відіграє важливу роль у підвищенні ефективності виробничої сфери через інтеграцію мобільних роботів [23,24]. Машинне навчання дозволяє роботам аналізувати та використовувати дані для вивчення патернів, прогнозування результатів та оптимізації процесів.…”

Towards next generation digital twin in robotics: Trends, scopes, challenges, and future

“…The robot model was visualized and could be controlled from its DT [100] . More recent practices included DT-integrated machine vision-based assessment of industrial robotic skills [101] , and industrial robot programming using machine learning enhanced point cloud information and RDT [102] . A critical shortcoming of data-driven RDT approach as hinted by [99] was lack of historical data.…”