The combination of AI and machine vision is generating more practical applications for collaborative robots (cobots), particularly in modern agricultural and manufacturing scenarios.
Maximum speed and detection rates of preferably 100 percent – these are the requirements for quality control in the packaging industry. INNDEO shows how these high requirements can be achieved with a sophisticated automation solution, based on machine vision and deep learning technologies.
Tintri, specialist in data management solutions for virtualised workloads, shares its predictions for 2024.
Robots and AI systems have become an integral part of industrial manufacturing sites. To ensure the safety of workers while using robots, a detailed risk assessment is necessary. This assessment is carried out from the perspective of functional safety, which examines the five main elements employed by robots to determine autonomous control actions: environment recognition, action planning, trajectory generation, motion control, and measurement.
It’s been a challenging few months for artificial intelligence (AI), as negative press stories about its potential misuse outweighed those advocating its power for good. Wider awareness and acceptance of AI will accelerate its uptake within automation, but we may have to use terms like deep and machine learning to avoid some of the misperceptions.
When the German government published its policy paper on Industry 4.0 in 2013, there was a sense of excitement about the future at COPA-DATA as terms such as IIoT and Smart Factory were being hotly debated. In this article, Stefan Reuther of COPA-DATA reflects on the past hype surrounding Industry 4.0 and asks, ‘where could it all lead next?’.