When AI paves the way for automation and other smart factory strategies, human resources are freed up to focus on big picture tasks, like higher-level decisions. You can future-proof your factory with deep learning technology and real-time computer vision applications in edge devices, allowing for greater accuracy in detection processes. Older methods use inflexible rule-based machine vision solutions for inspecting parts and detecting leaks, but Ultron’s deep learning approaches (like convolutional neural networks) are far more intuitive and accurate without compromising on production.

You can control Ultron with a sleek browser-based programming interface in modern programming languages like Javascript and Python. The software integrates I/O control, computer vision, and video analytics in the form of function libraries, I/O blocks, and APIs. NVIDIA hardware-accelerated SDKs such as the Isaac SDK for robotics algorithm development and the DeepStream SDK for streaming video analytics can be used to create high-performance AI applications. In terms of connectivity, the dual ethernet ports, serial ports, 5G/LTE, Wi-Fi, CANBus, and I2C options make Ultron an ideal PLC for multiple protocols and communication support.

Ultron VS Old PLCs
‍Older programmable logic controllers do not support camera input and cannot process videos and images. Established suppliers in the PLC market offer controllers with similar features for sensor fusion and relay controls in manufacturing processes.

Ultron is a more high-tech machine vision system that requires additional hardware and software integration between the machine vision cameras and the controller for image inputs and processing. Machine vision ensures quality control in high-precision manufacturing with sensors (cameras), processing hardware, and software algorithms to automate complex or mundane optical inspection tasks. They guide equipment during product assembly with precision. Ultron is an AI-enabled controller with sensor fusion capabilities that combines the above mentioned features into a single system.