Automated Path Planning for Control Cabinet Wiring with an Industrial Robot

The increase in hydrogen production capacity is seen as a main pillar for the reduction of greenhouse gas emissions by the German government. This paper presents a software architecture for automating the wiring process in control cabinet assembly, which is crucial for increasing productivity in electrolyzer production. A core element of the architecture is the Wiring Master, which is introduced to bridge digital planning data from the Digital Product Twin (DPT) and the real plant. The Wiring Master, interfacing with various subsystems, includes modules for path, trajectory, and wire planning. Sampling-based algorithms, particularly rapidly-exploring random tree with connect heuristic (RRT-connect), were tested and refined to achieve a reduction in manual robot programming for the control cabinet wiring. Real-world tests showed high success rates and feasibility for automated path planning with RRT-connect. However, the need for further optimization to address the simulation-to-reality gap and improve system reliability was identified.

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Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/iccar64901.2025.11073012
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Automated Path Planning for Control Cabinet Wiring with an Industrial Robot

Milan Brisse, Bernd Kuhlenkötter, Elías Milloch, Fabian Vacha
Robotic Path Planning Algorithms
article

Automated Path Planning for Control Cabinet Wiring with an Industrial Robot

Milan Brisse, Bernd Kuhlenkötter, Elías Milloch, Fabian Vacha
article en

Abstract

The increase in hydrogen production capacity is seen as a main pillar for the reduction of greenhouse gas emissions by the German government. This paper presents a software architecture for automating the wiring process in control cabinet assembly, which is crucial for increasing productivity in electrolyzer production. A core element of the architecture is the Wiring Master, which is introduced to bridge digital planning data from the Digital Product Twin (DPT) and the real plant. The Wiring Master, interfacing with various subsystems, includes modules for path, trajectory, and wire planning. Sampling-based algorithms, particularly rapidly-exploring random tree with connect heuristic (RRT-connect), were tested and refined to achieve a reduction in manual robot programming for the control cabinet wiring. Real-world tests showed high success rates and feasibility for automated path planning with RRT-connect. However, the need for further optimization to address the simulation-to-reality gap and improve system reliability was identified.

Ruhr University Bochum (DE)
Sustainable cities and communities
Openalex Percentile: Top 85%
Robotic Path Planning Algorithms
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