Artificial intelligence (AI) has been a hype topic for several years now. According to Omron, AI is set to reach the next level in 2020: In this context, new practical solutions for industrial automation should focus on complementing human intelligence and skills, keyword augmented intelligence instead of artificial intelligence. In addition, the focus should increasingly be on how AI can be used specifically and efficiently on the factory floor while also supporting sustainability. The focus is on solutions that measurably support employees in industrial automation while also improving decision-making and operational efficiency.

Companies should therefore keep an eye on the following AI developments in robotics and industrial automation:

1. machine data generated "at the edge"
A new generation of employees in the field of industrial automation will change jobs more frequently than ever before. Developments in factories are focusing on the generation and collection of in-depth knowledge and data insights at machine level - in other words, "at the edge". The machine can learn from its human operators and then improve performance. AI-controlled technology, or machine learning, makes it possible to predict both product and device failures using data generated by Industrial Internet of Things (IIoT) devices. By analyzing combined data, users can quickly predict potential machine failures to avoid malfunctions and degraded product quality.

2. efficiency improvements through self-learning algorithms
With the shift from mass customization to a high-mix, low-volume approach (batch size 1), efficiency must be improved by reducing human error and machine downtime. AI and learning algorithms can help machine operators to achieve the best result with every changeover. Innovative control technology also supports employees to work hand in hand with robots and machines to achieve manufacturing excellence. This is achieved by using a wide range of factory automation equipment that enables IIoT-enabled production or implements optimal AI algorithms in the equipment. Control systems equipped with AI are designed to recognize signs of irregularities immediately. AI algorithms in the machine automation controller enable it to learn the repeated movements of equipment from precise sensor data. This in turn provides feedback for condition monitoring and real-time control of machines.

3. optimized decision making based on visualized data
Industry 4.0 and IIoT enable the accurate collection of historical data. However, many AI projects struggle with the visualization of new data. Predictive maintenance solutions, such as Omron's Sysmac AI Controller, can synchronize the control functions of production lines and equipment with AI processing in real time. The AI controller can support companies by generating new and non-historical data that can be visualized time-stamped. The process of collecting raw data from machines is fully automated by the AI controller "at the edge". This leads to greater data accuracy and consistency. The controller also creates data models based on correlation analyses and monitors the machine status based on these models. Without this automation, machine designers and operators would have to invest in developing their own analysis and optimization capabilities.

4. sustainable technology
The rapid growth of the world's population is putting immense pressure on the environment. AI-supported collaborative robots(cobots) will become increasingly important in 2020 and beyond to meet this challenge. The ultimate goal is to create healthy and safe living and working conditions that have less impact on the environment. Omron helps companies achieve more sustainable working conditions in factories with its portfolio of robots and AI. Assembly and disassembly robots play an important role in this. The new generation of robots can learn from machine operators (sensing). They can work together with the TM Cobot (Control) on a circular production line. They collect intelligent and intuitive data about their actions, evaluate the data using algorithms, advise the employee on the next steps and implement efficient processes for each changeover (Think). as