Autonomy and adaptivity are conditions for production systems that react to their environment and new products in an agile and robust manner. Those types of systems must perceive and interpret their environments and products and plan their own actions and processes autonomously. One of the key technologies identified for implementing autonomous production systems is the dynamic identification and interpretation of a changeable working environment in 3D.

Essentially, with a 3D image processing system, products, obstacles and technical equipment in the working space of the inspection robot are dynamically identified by 3D laser sensors. The complexity of an object (degree of concavity, undercuts, reflection properties) is determined from the product information (CAD) or the first scan. The more complex an object, the more scans are necessary to obtain sufficient geometry information on the object. The recognition process is performed on the 3D point cloud that was acquired that way. The recognition process identifies objects in the 3D point cloud, i.e. CAD models in a database are identified as relevant objects in the scanned working space and matched in the correct 3D position in the point cloud. The result is automatic 3D modelling of the working space.

3D recognition of the working space

True autonomy requires robust (visual) dynamic identification and interpretation of the changeable working environment in 3D.

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• Development of robot concepts up to the implementation of innovative solutions
• From the idea to transfer to series production
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• Feasibility studies
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• Easily extensible 
• Integration with existing production lines