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Complex use cases require sophisticated know how

The automation problems we tackle have one thing in common: They are enormously complex, require non-repetitive motion patterns and deal with dynamic process parameters. Some use cases even demand multiple robots to work alongside each other in the same workspace. This complexity asks for advanced robotic solutions that exceed the state of the art. sewts robotics team develops these technologies in-house. Our core competencies include on-the-fly motion and trajectory planning, real-time control of various industrial robots as well as multi robot coordination.

This is where the real magic happens. Smart algorithms are needed to build adaptive systems that can cope with non-deterministic automation processes. That’s why we leverage the latest AI research findings, refine them for our needs and finally put them together to one big piece – our robotic brain. It receives diverse sensor data (e.g. optical information), draws conclusions on a human-like level of cognition and translates these into high-level robotic commands. That’s how our systems can complete tasks that actually require human intellect.

Visual information is the most important input for our robotic brain and therefore it is crucial to always provide high quality data. Depending on the use case we apply state of the art 2D or 3D vision systems – both can be seamlessly integrated in our systems. We are experts in enhancing the generated data which is especially relevant when working with 3D point clouds. This preprocessing is a vital building block of our systems in terms of generating usable input for our artificial intelligence.

The majority of our use cases deals with flexible materials, textiles and alike. It is essential to understand the characteristics of these materials in order to implement robust processes. We achieve that by highly sophisticated material simulations. sewts develops specialized FE simulations in cooperation with TUM to reproduce the behavior of textiles. These simulations open up completely new possibilities regarding the development of smart algorithms. We use them to generate synthetic training data for our AI engineering to name just one example – dramatically shortening our development cycles.