Videos & Latest News

Roboception Vision Solutions Now Part of the Franka Ecosystem

Great news for 2023:  Franka Production 3 and Franka Research 3 robots can now be enhanced by Roboception’s ‘Eyes and Brains for Robots’. Roboception's vision solutions are part of the Franka ecosystem. Highly efficient pick-and-place applications, machine tending, bin picking or (de)palletizing have just become that much easier for your most recent Franka robots. For the quick and easy integration of Roboception‘s rc_visard 3D stereo sensor and innovative rc_reason software modules, Franka provides easy-to-use apps for the Desk software. rc_visard owners that want to use a Franka robot of the latest generation can simply purchase the license for the Roboception Apps and download them via the Franka World. We recommend to also update the rc_visard with the latest firmware. If you do not have an rc_visard yet, just contact us via sales@roboception.de. Watch Franka's video about the cooperation:

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rc_reason BoxPick in Action: “Our Customer is Very Impressed.”

Of course, bricks aren't exactly boxes - nonetheless, their rectangular shape is what makes them ideal for a successful application of Roboception's BoxPick solution: In this newly released use case, learn how Kautenburger GmbH (Germany) implemented a vision component for Refrectarios Kelsen S.A. (Spain) that supports the automated de-palletizing of oven bricks from wagons, nevermind the fact that these bricks come in 100+ different shapes and sizes, and tend to change shape and position ever so slightly throughout their production process. Read how a seemingly simple and cost-efficient modification, flawlessly implemented, reduced the cycle time for a pick-and-place from 18 to nine seconds and hence allows for more efficient handling.

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KI4MRK – Research Project Successfully Completed

Nowadays, cobots can work together with humans in logistics or production without protective fences. Human-robot collaboration (HRC) is subject to strict standards and rules to ensure the safety of humans at all times. As a result, common HRC systems have to operate at low, reduced speeds or even stop completely when a human approaches.The goal of the KI4MRK research project, funded by the German "Bundesministerium für Bildung und Forschung", was to develop human motion prediction using the combination of three deep neural networks (NN). For this purpose, the joint workspace was transformed into a block representation (voxel representation). This allows obstacles to be represented as volumes. Using autoencoders, human poses were preprocessed in such a way that they can be efficiently stored in the system. A second autoencoder is trained using public motion databases, allowing prediction of individual motions. In the final step, a recurrent neural network trained with only a small amount of task-specific data thanks to long short-term memory (LSTM) can still predict complex actions. The developed AI-based motion prediction of actions in voxel space, combined with dynamic task scheduling, allows more efficient design of HRC-systems.Furthermore, they can be used more effectively and economically by minimizing stop times. After having successfully evaluated this motion prediction technique using two demonstrators last year, we look forward to exploring the further procedural indications of this method. The results we were able to achieve together with our partners are summarized in this video:

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