Baden-Württemberg
AI-Supported Robot Trajectory Optimization
(Chenwei Sun @ FZI / KAUTENBURGER)
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Predictive Analytics for Autonomous Mobile Robots
(Carlo Fitz, ARCULUS)
NEED for AI:
The limited stiffness of the robots might cause machining inaccuracies. In order to correct these errors at runtime, complex models, which approximate the processing forces and stiffness of the robot are necessary.
AI REGIO SOLUTION:
In the context of this project it is examined whether the errors can be mapped with the help of a machine learning approach. For this purpose, a robot with a processing spindle is absolutely measured at runtime. The trajectory thus determined is then compared with the TARGET trajectory, the TARGET 3D model and the 3D scan of the ACTUAL model. Based on the resulting deviations, a possible optimization is then derived with the help of AI methods, which then makes it possible to maintain the desired trajectory even without absolute measurement of the TCP at runtime.
EXPECTED BENEFITS - The proposed solution offers:
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improved accuracy of robots in CNC machining
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generalization of robot stiffness modelling through AI methods
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reduction of the number of calibration manoeuvres needed to train an AI model on a robot type
Predictive Analytics for Autonomous Mobile Robots
ARCULUS works on the production logistics of the future and soon it will use a modular production grid, conveying products and materials through production on autonomous logistics robots.
Autonomous mobile robots are the technology foundation to build sophisticated real-time controlled production systems, which are able to increase worker efficiency by up to 30 % in manufacturing.
NEED for AI:
In order to manage a highly scalable robotics portfolio predictive analytics with self-learning algorithms is essential.
AI REGIO SOLUTION:
The solution will be integrated in all robotic applications.
EXPECTED BENEFITS
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improved maintenance process and high customer acceptance to improve the adoption rate of autonomous transportation in manufacturing environments