Bernd Meese

Fraunhofer Institute for Manufacturing Engineering and Automation

Papers

Total Citations

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About

Bernd Meese is a leading researcher at the intersection of intelligent automation and sustainable manufacturing, with key contributions in reinforcement learning for robotic inspection and automated disassembly of battery systems. His most influential work, "A Reinforcement Learning Approach to View Planning for Automated Inspection Tasks" (2021, 30 citations), pioneers the use of RL to solve complex view-planning challenges in flexible production facilities, enabling more reliable and cost-effective automated inspection of small-lot workpieces. This approach addresses critical industry needs where manual inspection is both expensive and error-prone. Meese also tackles pressing sustainability challenges in his highly relevant work "Automated Disassembly of Battery Systems to Battery Modules" (2024, 10 citations), which develops flexible automation solutions for recovering valuable raw materials like lithium and cobalt from electric vehicle batteries. This research directly confronts product-specific hurdles such as high-voltage safety, state variance, and labor shortages. By combining reinforcement learning with industrial robotics, Meese is advancing both smart manufacturing and circular economy practices, making his work essential reading for researchers in automation, robotics, and sustainable production systems.