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About

Michael Schwegel is a robotics researcher whose work spans the critical intersection of advanced control, kinematics, and novel mechanical design. His primary research areas include redundant manipulator control, cable-driven parallel robotics, and human-robot collaboration. Schwegel’s most impactful contribution is a machine learning framework for solving the analytical inverse kinematics of redundant manipulators in real time, a notoriously difficult problem that his 2023 paper (37 citations) addresses by selecting the optimal solution for application-specific demands. He has also made significant strides in mechatronic design, introducing a novel class of planar cable-driven parallel robots that achieve arbitrary end-effector rotation through a simple, robust cable-wrapping mechanism—a concept detailed across multiple publications (13 and 4 citations). His work on collaborative synchronization (5 citations) demonstrates practical human-robot interaction, enabling a 7-axis robot to draw on a moving object held by a human. Most recently, Schwegel has developed a computationally efficient path iterative learning controller for industrial robots (3 citations), experimentally validating a scheme that combines model-based control with online learning to enhance absolute accuracy.