Max Robotics — Global Service Robot Directory + US Certification

About

Andreas Britzelmeier is a researcher specializing in optimal control, model-predictive control (MPC), and trajectory planning for autonomous systems. His work bridges theoretical numerical optimization and practical robotics applications, with a particular focus on solving complex real-world control problems in dynamic, constrained environments. Britzelmeier's most impactful contribution, garnering 34 citations, introduced a nonsmooth Newton method for linear model-predictive control applied to mobile robot tracking with obstacle avoidance — an elegant approach that handles both fixed and moving obstacles while managing the inherent nonlinearities of robotic systems. His research extends naturally into multi-agent settings, with work on interacting vehicle control combining MPC, generalized Nash equilibrium problems, and dynamic inversion to enable collision-free coordination within road networks. More recently, Britzelmeier has advanced trajectory planning methodologies, exploring direct optimal control techniques for cluttered, high-dimensional spaces and developing an adaptive mesh dynamic programming algorithm for robotic manipulator motion planning. These contributions address critical limitations of general-purpose solvers in complex environments. Collectively, his publications reflect a researcher dedicated to making autonomous systems safer and more computationally tractable, with growing influence across robotics, autonomous vehicles, and applied mathematical optimization communities.