Use case

automation for


To date, a large portion of industrial automation is based on logic-based control from PLCs for a variety of processes and systems. The fundamental role of a PLC is to automate processes by sending programmed control functions computed from signals received from connected inputs such as a sensor, switch, thermometer, or relay to output devices. This system architecture, also known as a supervisory control and data acquisition (SCADA) system, is a secure and standardized automation infrastructure for sharing diagnostics and analytics. For example, PLCs are often used in industrial machine vision and automation systems for image processing and quality inspection, in pick-and-place operation of autonomous forklifts and robotic arms in manufacturing environments, as well as in downstream to upstream process control in oil and gas plants.

PLC implementation in action

PLCs have traditionally been widely used due to their dependability and robustness, as they require minimal intervention to operate. Optical quality inspection using machine vision systems is one of the most widely used methods for ensuring quality control in high-precision manufacturing. In production, for example, machine vision systems use sensors (cameras), processing hardware and software algorithms to automate complex or mundane optical inspection tasks and precisely guide handling equipment during product assembly.

Bringing deep learning to the factory floor

With the emergence of Industrial Internet-of-Thing (IIoT), Artificial Intelligence-of-Thing (AIoT) and robotics, manufacturers are developing smart factory strategies that use AI on automated manufacturing processes in production environments, freeing up people to focus on handling exceptions and making higher-level decisions in order to remain competitive in the industry. For example, to achieve higher detection accuracy, manufacturers are beginning to use deep learning technologies and real-time computer vision applications that can be performed at the edge. Traditional inspection tasks use rules-based machine vision solutions for part inspection and leak detection, but deep learning approaches such as convolutional neural networks (CNNs) can help to optimize inspection accuracy without any impact on production.

Ultron for smarter factories

To address this, SmartCow designed an all-in-one control platform that combines a wide range of industrial functionalities such as machine vision, PLC, AIoT, and robotics, complemented by a control software. This control software integrates I/O control, computer vision, and video analytics by providing the necessary vision and PLC capabilities in the form of function libraries, I/O blocks, and APIs that can be called up from Ultron, all configurable through a browser-based programming tool, allowing engineers to program in modern programming languages such as JavaScript and Python. Furthermore, 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, 5G/LTE, Wifi, CANBus, and I2C options make Ultron an ideal PLC for multiple protocols and communication support.


To summarize, Ultron is built to improve industrial automation processes that lack the agility, programmer productivity, and development scalability needed for effectively interacting with sophisticated next-generation AI computing workloads that are often deployed on the cloud. We discovered that advanced computing capabilities are in high demand at the industrial edge control system. Nonetheless, SmartCow's approach of using Jetson-based programmable logic controllers to easily integrate edge AI with flexible industrial automation technology is applicable to a wide range of industries beyond factory automation, and it is the company's goal to innovate next generation solutions toward industry 4.0.