Bart Van Doninck
Papers
3
Total Citations
28
H-Index
2
About
Bart Van Doninck is a researcher at the forefront of human-robot collaboration and intelligent manufacturing systems. His work centers on integrating reinforcement learning and digital support tools to optimize complex, real-world production environments. Van Doninck’s most impactful contribution is his 2022 study on a Q-Learning algorithm for flexible job shop scheduling, which tackles the challenge of coordinating a two-armed robot and a human operator in an assembly task for light switches. This paper, with 22 citations, demonstrates how AI can generate efficient schedules for shared workspaces. He has also explored the human side of automation, investigating the relationship between worker stress and the acceptance of collaborative robots in a 2024 pilot study. His most recent 2025 work introduces the concept of a "Digital Colleague"—an intuitive operator support system designed for the high-mix, low-volume (HMLV) production environments that define modern customization demands. By blending algorithmic scheduling with human factors research, Van Doninck is helping shape a future where humans and machines work together seamlessly, efficiently, and safely.
Research Focus
Key Achievements
Top Papers
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