KoARob: Towards AI-based Safety in Human-Robot-Collaborations

As industries face changes in population and the need for better production efficiency arises, combining human workers with robots is becoming more common. In such collaborations, besides the user experience, the safety of the human worker is a critical aspect. This paper introduces an AI-based safety system designed to maintain a safe distance from human workers to prevent injuries. To circumvent the limitations of similar safety approaches, the system uses redundant methods to detect humans and their various joints. An evaluation in a real-world scenario shows that such an AI-based system can reliably detect humans and stop robots before a collision occurs. This work proves that using AI-based systems for human detection in safety-related contexts is not impossible and creates a basis for a new generation of safety systems that can enhance future human-robot collaborations.

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Publication Details

Published
2025-08-17
DOI
https://doi.org/10.1109/case58245.2025.11164087
Citations
1
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
1.54
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article

KoARob: Towards AI-based Safety in Human-Robot-Collaborations

Daniel Bermuth, Wolfgang Reif, Alexander Poeppel
1 citations
Occupational Health and Safety Research
1.54
article

KoARob: Towards AI-based Safety in Human-Robot-Collaborations

Daniel Bermuth, Wolfgang Reif, Alexander Poeppel
article en
1 citations

Abstract

As industries face changes in population and the need for better production efficiency arises, combining human workers with robots is becoming more common. In such collaborations, besides the user experience, the safety of the human worker is a critical aspect. This paper introduces an AI-based safety system designed to maintain a safe distance from human workers to prevent injuries. To circumvent the limitations of similar safety approaches, the system uses redundant methods to detect humans and their various joints. An evaluation in a real-world scenario shows that such an AI-based system can reliably detect humans and stop robots before a collision occurs. This work proves that using AI-based systems for human detection in safety-related contexts is not impossible and creates a basis for a new generation of safety systems that can enhance future human-robot collaborations.

University of Augsburg (DE)
Openalex Percentile: Top 14%
Occupational Health and Safety Research
1.54
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