3 papers
math.OC2026
Provably Safe Decentralized Contingency MPC under State-Only Information and Limited Sensing for Nonlinear Multi-agent Systems
Max Studt, Georg Schildbach
This paper considers decentralized contingency MPC for multi-agent control under a state-only information pattern, with particular focus on limited sensing and plug-and-play operat…
math.OC2026
Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
Max Studt, Georg Schildbach
Decentralized collision avoidance remains challenging, particularly when agents do not communicate any information related to planned trajectories. Most existing approaches either…
eess.SY2025
Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Max Studt, Georg Schildbach
Achieving safe and coordinated behavior in dynamic, constraint-rich environments remains a major challenge for learning-based control. Pure end-to-end learning often suffers from p…