Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Carron, Andrea"'
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
Autor:
Baumann, Nicolas, Ghignone, Edoardo, Hu, Cheng, Hildisch, Benedict, Hämmerle, Tino, Bettoni, Alessandro, Carron, Andrea, Xie, Lei, Magno, Michele
Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner that learns the behavior of opponents through Gaussian Process (GP) regressi
Externí odkaz:
http://arxiv.org/abs/2410.04868
We propose a novel data-driven stochastic model predictive control framework for uncertain linear systems with noisy output measurements. Our approach leverages multi-step predictors to efficiently propagate uncertainty, ensuring chance constraint sa
Externí odkaz:
http://arxiv.org/abs/2409.10405
We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-time stochastic linear systems with output measurements and provides a
Externí odkaz:
http://arxiv.org/abs/2407.17277
Autor:
Bodmer, Sabrina, Vogel, Lukas, Muntwiler, Simon, Hansson, Alexander, Bodewig, Tobias, Wahlen, Jonas, Zeilinger, Melanie N., Carron, Andrea
This paper presents an open-source miniature car-like robot with low-cost sensing and a pipeline for optimization-based system identification, state estimation, and control. The overall robotics platform comes at a cost of less than $700 and thus sig
Externí odkaz:
http://arxiv.org/abs/2404.08362
Autor:
Pabon, Luis, Köhler, Johannes, Alora, John Irvin, Eberhard, Patrick Benito, Carron, Andrea, Zeilinger, Melanie N., Pavone, Marco
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more co
Externí odkaz:
http://arxiv.org/abs/2404.01550
Autor:
Krinner, Maria, Romero, Angel, Bauersfeld, Leonard, Zeilinger, Melanie, Carron, Andrea, Scaramuzza, Davide
Publikováno v:
Robotics: Science and Systems (RSS), 2024
Quadrotor flight is an extremely challenging problem due to the limited control authority encountered at the limit of handling. Model Predictive Contouring Control (MPCC) has emerged as a promising model-based approach for time optimization problems
Externí odkaz:
http://arxiv.org/abs/2403.17551
Autor:
Baumann, Nicolas, Ghignone, Edoardo, Kühne, Jonas, Bastuck, Niklas, Becker, Jonathan, Imholz, Nadine, Kränzlin, Tobias, Lim, Tian Yi, Lötscher, Michael, Schwarzenbach, Luca, Tognoni, Luca, Vogt, Christian, Carron, Andrea, Magno, Michele
Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their limits, many existing full-system solutions necessitate complex, cust
Externí odkaz:
http://arxiv.org/abs/2403.11784
In recent years, the increasing need for high-performance controllers in applications like autonomous driving has motivated the development of optimization routines tailored to specific control problems. In this paper, we propose an efficient inexact
Externí odkaz:
http://arxiv.org/abs/2401.02194
Autor:
Nghiem, Truong X., Drgoňa, Ján, Jones, Colin, Nagy, Zoltan, Schwan, Roland, Dey, Biswadip, Chakrabarty, Ankush, Di Cairano, Stefano, Paulson, Joel A., Carron, Andrea, Zeilinger, Melanie N., Cortez, Wenceslao Shaw, Vrabie, Draguna L.
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As oppos
Externí odkaz:
http://arxiv.org/abs/2306.13867
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation
Externí odkaz:
http://arxiv.org/abs/2304.01649