A Distributed Computing Real-Time Safety System of Collaborative Robot

Abstract

Robotization has become common in modern factories due to its efficiency and cost-effectiveness. Lots of robots and manipulators share their workspaces with humans what could lead to hazardous situations causing health damage or even death. This article presents a real-time safety system applying the distributed computing paradigm for a collaborative robot. The system consists of detection/sensing modules connected with a server working as decision-making system. Each configurable sensing module pre-processes vision information and then sends to the server the images cropped to new objects extracted from a background. After identifying persons from the images, the decision-making system sends a request to the robot to perform pre-defined action. In the proposed solution, there are indicated three safety zones defined by three different actions on a robot motion. As identification method, state-of-the-art of Machine Learning algorithms, the Histogram of Oriented Gradients (HOG), Viola-Jones, and You Only Look Once (YOLO), have been examined and presented. The industrial environment tests indicated that YOLOv3 algorithm outperformed other solutions in terms of identification capabilities, false positive rate and maximum latency.

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

“…Gradolewski et al report a safety system for a collaborative robot that has the ability to prevent human health damage [55]. It is developed upon techniques from distributed computing, machine and deep learning, computer vision and sensing, robot motion control.…”

Comprehensive Bird Preservation at Wind Farms

“…Moreover, the system should assure a high reliability of detection, identification and classification without compromising the needs of low purchase cost along with installation and maintenance costs. To assure a real-time operation mode, the proposed solution applies distributed computing into the IoT paradigm [54]. With a stereoscopic vision acquisition system, AI-based identification and size classification algorithms.…”

A Runway Safety System Based on Vertically Oriented Stereovision

“…The bird detection in the video stream can be made using motion detection [ 22 , 25 , 43 ], AI based identification [ 26 , 44 , 45 , 46 , 47 , 48 , 49 ], or a combination of both [ 10 , 38 ]. Whereas the motion detection algorithms allow the reduction of the computational complexity of the safety system [ 50 ], the application of AI methods allows bird identification [ 48 , 49 ] and the reduction of false positive rates [ 10 ]. From the AI based solutions, the Convolutional Neural Networks (CNNs) [ 51 , 52 , 53 ] outperform other methods, for instance the Haar feature based cascade classifier [ 45 , 54 ] or Long Short-Term Memory (LSTM) [ 48 ].…”

“…On its left side, there are the control unit along with the sensors and actuators responsible for data acquisition and system reactions. The system is based on the IoT and distributed computing concepts [ 50 ], facilitating communication between modules and providing easy access to the storage data through the intuitive GUI .…”