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Commercially available mobile robots must behave as intended in a wide variety of environments, especially in outdoor applications. With the multitude of potential application environments, the search for errors for these systems becomes a search for a needle in a haystack.
A digital twin was used for the robot and its operating environment. An AI-based test automation was applied to this environment, which self-learns to identify the critical deployment scenarios for the robot and thus detects the fault cases that need to be remedied.
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