AI-Based Learning Approach with Consideration of Safety Criteria on Example of a Depalletization Robot

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.

Design of a Deep Post Gripping Perception Framework for Industrial Robots

“…Machine learning, which is the use of learning agents that improve their performance on future tasks after making observations about the world [10], and mainly deep learning, meaning to learn using artificial neural networks with hidden layers, has known a wide application spectrum [11] and an important success recently [12]. The use of this artificial intelligence ability increases the autonomy of robots and their ability to adapt to new conditions, which allow their usage in novel applications [13][14][15][16].…”