#mpcc

Check out our #RSS2024 paper "#MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints." Model Predictive Contouring Control (MPCC) has shown promising results for agile robotics applications, including car and drone racing. Existing approaches struggle to introduce safety considerations, often resulting in crashes. What does it take to drive or fly fast and safe? We enhance our former MPCC by incorporating spatial constraints that reliably prevent obstacle collisions, allowing planning the fastest trajectory within these safety limits. To improve performance, we leverage real-world data to refine the dynamic model. Our approach is the first to achieve a 100% success rate in real-world experiments. This safety benefit comes without compromising performance, as our method achieves lap times comparable to the best-performing state-based #ReinforcementLearning (RL) policies. Reference M. Krinner, A. Romero, L. Bauersfeld, M. Zeilinger, A. Carron, D. Scaramuzza, "MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints" Robotics, Science and Systems, 2024 PDF: Video: Kudos to Maria Krinner, Angel Romero Aguilar, Leonard Bauersfeld, Melanie Zeilinger, Andrea Carron! Ángel Romero Leonard Bauersfeld University of Zurich UZH Science UZH Space Hub AUTOASSESS European Research Council (ERC) #MPC #ModelPredictiveControl