Game changer: computer vision in shopfloor automation

Robotics 4.0 in focus

Automation & robotics has been a key topic at inpro for many years, which we continue to develop in partnership-based innovation projects and put into practice with specific digital solutions such as ginero – multi-robot cell planning for system planners.

Robotics trends 2030: Flexibility in automation

According to the latest report from the International Federation of Robotics, computer vision is one of the most important drivers of innovation in modern robotics after artificial intelligence. The average annual growth rate of this technology is 18%. Mordor analysts forecast a market volume of 40 billion US dollars for the year 2029. Other robotics trends such as mobile manipulators and the digital twin cannot be viewed in isolation from modern sensor technology and machine vision. Together, these technologies contribute to intelligent, autonomous industrial robots and can address challenges such as the shortage of skilled workers and cost pressure in a solution-oriented manner.Fast progress in digitalization and investments can currently be observed in robot-assisted handling, as e.g. in order picking systems in logistics.

Manual vs. automated parts handling in automotive production

Machine learning of computer vision robot systems

The establishment of vision systems in the manufacturing industry

While the layout planning of production cells and the offline programming and validation of robots is still carried out as standard today without the special integration of image processing systems, we assume that this will be an integral part of the planning phase in the near future. Several technological trends are helping to ensure that this combination for safeguarding computer vision robots in engineering will quickly become established and that nothing will stand in the way of these systems becoming established on the store floor. In detail, these are

Thanks to the progress and development of new methods in design automation, the complexity of creating layout plans for production planners has already been significantly reduced, so that new parameters and issues such as planning the use of sensors can be taken into account more easily.

Introducing technological innovations into existing manufacturing processes is risky and the test phase in real factory environments is costly. State-of-the-art simulation solutions such as the Simcenter of Siemens also enable realistic simulation using physics engines.

The training of vision systems requires large amounts of data that are difficult for individual companies to provide or can only be provided over a longer period of time. This bottleneck can be overcome by using synthetic data.

Collaborative Incubation: Membership model for industrial innovators

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2024-10-02T12:02:21+02:00
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