Andreas Aurnhammer

KUKA (Germany), Universität der Bundeswehr München

Papers

Total Citations

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About

Andreas Aurnhammer’s research focuses on optimal trajectory planning and energy-efficient control for industrial robots, with a strong emphasis on real-time and robust optimization. His most impactful work, published in 2016 as part of the EU project AREUS, introduces an optimization procedure that reduces energy consumption by up to 30% and peak power by up to 60% for existing time-optimal robot trajectories, validated on real industrial robots. This paper has garnered 20 citations, highlighting its practical significance in sustainable manufacturing. Aurnhammer’s earlier contributions, including adaptive and robust stochastic trajectory planning (2001–2004), address the challenge of controlling robots under uncertain model parameters, laying groundwork for reliable automation. His work bridges theoretical optimization with real-world application, offering tangible solutions for reducing operational costs and energy use in robotics. Aurnhammer’s research is particularly valuable for students and engineers seeking to enhance robot efficiency without sacrificing performance, making him a key figure in the field of industrial robotics and optimal control.