Mark Wielitzka
Papers
3
Total Citations
10
H-Index
2
About
Mark Wielitzka is a researcher at the forefront of intelligent automation, whose work bridges the gap between advanced control theory and practical industrial applications. His research focuses on three key areas: the identification of complex, non-linear systems for drive trains, the coordination of multi-robot formations, and the probabilistic modeling of smart manufacturing processes. Wielitzka’s major contribution lies in developing a "bright-grey box" approach using degenerate genetic programming to simultaneously identify both the structure and parameters of hard non-linearities in industrial drive trains, a method that dramatically simplifies the commissioning of robots and gantries. In the domain of multi-robot systems, he has pioneered LiDAR-based localization for formation control, enabling groups of mobile robots to function as a single, cohesive virtual unit. His work on probabilistic simulation further advances smart manufacturing by applying machine learning to model sojourn time distributions. While his most-cited papers (totaling 10 citations) represent foundational work, their impact is amplified by their direct relevance to Industry 4.0, offering scalable solutions for the next generation of autonomous and interconnected production systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2LiDAR-Based Localization for Formation Control of Multi-Robot Systems3 citations · 2022
- 3