AI-based learning approach with consideration of safety criteria on example of a depalletization robot
Jocas, Mark (1); Kurrek, Philip (1); Zoghlami, Firas (1); Gianni, Mario (2); Salehi, Vahid (1)
Type:
Year:
2019
Editor:
Wartzack, Sandro; Schleich, Benjamin; Gon
Author:
Series:
ICED
Institution:
Munich University of Applied Sciences
Section:
Industry 4.0
DOI number:
ISSN:
2220-4342
Abstract:
Robotic systems need to achieve a certain level of process safety during the performance of the
task and at the same time ensure compliance with safety criteria for the expected behaviour.
To achieve this, the system must be aware of the risks related to the performance of the task
in order to be able to take these into account accordingly. Once the safety aspects have been
learned from the system, the task performance must no longer influence them. To achieve this,
we present a concept for the design of a neural network that combines these characteristics.
This enables the learning of safe behaviour and the fixation of it. The subsequent training of
the task execution no longer influences safety and achieves targeted results in comparison to a
conventional neural network.
task and at the same time ensure compliance with safety criteria for the expected behaviour.
To achieve this, the system must be aware of the risks related to the performance of the task
in order to be able to take these into account accordingly. Once the safety aspects have been
learned from the system, the task performance must no longer influence them. To achieve this,
we present a concept for the design of a neural network that combines these characteristics.
This enables the learning of safe behaviour and the fixation of it. The subsequent training of
the task execution no longer influences safety and achieves targeted results in comparison to a
conventional neural network.
Keywords:
