Alexander Joos
Papers
1
Total Citations
2
H-Index
1
About
Alexander Joos is a researcher at the intersection of robotics, control theory, and human motion modeling. His primary contributions lie in developing advanced trajectory planning algorithms that enable robots to move more naturally and predictively in human environments. In his highly cited 2018 work, "Human Center of Mass Trajectory Models Using Nonlinear Model Predictive Control," Joos introduced a novel NMPC scheme that models human center of mass trajectories in obstacle-rich settings. This dual-purpose model allows robots to both plan their own motion in a human-like manner and predict human movement, a critical capability for safe human-robot interaction. While his citation count is still growing, the foundational nature of this work—bridging biomechanics and real-time control—positions it as a key reference for researchers in autonomous navigation and collaborative robotics. Joos’s approach stands out for its practical applicability, offering a framework that can be deployed onboard robots for real-time decision-making. His research continues to influence the development of more intuitive, socially aware robotic systems.
Research Focus
Key Achievements
Top Papers
- 1