Thanks to the technology advances and discoveries of the past few years, robots are becoming more sophisticated, while end users are demanding higher accuracy, speed, programmability, autonomy, and more.
Collaborative robots hold the potential to increase productivity by up to 20%.
Robots are evolving into intelligent, autonomous machines that make decisions at the electromechanical edge, where most processing of the massive amounts of available data is happening — but data is also integrated and analyzed in a larger cloud infrastructure. This presents both opportunities and challenges for robot manufacturers and solution providers, as they connect these two worlds.
Integrating more intelligence closer to the edge reduces data transfer. It also means traditional automation technology must be augmented with real-time AI.
What you will learn:
- How you can build a solution designed to operate at the edge and easily connect to cloud environments
- What level of security is needed to prevent disruption of industrial workflows
- The place of AI and machine learning in robotics applications
- What you should look for in your real-time technology stack in order to make real-time AI systems possible