Papers
2
Total Citations
47
H-Index
2
About
Lucas Weihmann is a researcher specializing in robotics and optimization, with a focus on enhancing the force capabilities of humanoid and parallel manipulator systems. His work centers on applying advanced evolutionary algorithms to solve complex mechanical design problems, particularly through the use of modified self-adaptive differential evolution techniques. In his most-cited paper (2016, 26 citations), Weihmann developed a method to optimize the static force capability of humanoid robots, enabling more efficient and stable locomotion and manipulation. His earlier 2011 study (21 citations) extended this approach to planar parallel manipulators, demonstrating the versatility of his optimization framework. These contributions have provided practical tools for improving robotic performance in tasks requiring precise force control, such as industrial automation and assistive robotics. Weihmann’s research bridges theoretical algorithm development with real-world robotic applications, offering valuable insights for engineers and researchers seeking to push the boundaries of robotic dexterity and efficiency.
Research Focus
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Top Papers
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