Papers
3
Total Citations
35
H-Index
3
About
G. Seeger’s research focuses on the practical control and optimization of industrial manipulators, with a particular emphasis on dynamic modeling and motion planning. His most cited work, “Estimation of rigid body models for a six-axis manipulator with geared electric drives” (2003, 20 citations), provides several experimentally validated approaches for estimating link inertia, gravity loading, and friction coefficients—key contributions that bridge the gap between theoretical robot dynamics and real-world implementation. In “Optimizing robot motion along a predefined path” (2005, 10 citations), Seeger developed a computationally efficient control scheme that enables manipulators to move at maximum speed within velocity and acceleration constraints, adapting to any given path while requiring minimal computing power. His earlier work, “Self-Tuning Control of a Commercial Manipulator Based on an Inverse Dynamic Model” (1991, 5 citations), laid foundational groundwork for adaptive control in robotics. Seeger’s contributions are particularly notable for their emphasis on experimental evaluation and practical applicability, making his methods directly useful for engineers working with conventional geared electric drives. His research remains relevant for students and practitioners seeking robust, implementable solutions for robot motion control and parameter estimation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Optimizing robot motion along a predefined path10 citations · 2005
- 3