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Deep Learning for Model Predictive Contouring Control
Model Predictive Contouring Control (MPCC) has shown to achieve very good results in the task of time-optimal multi-waypoint flight. MPCC methods have the freedom to select the optimal states of the system at runtime, dropping the need for a computationally expensive reference trajectory. Our recent work shows MPCC can achieve better lap times than state-of-the-art planning+tracking approaches, and that the method can be run in real-time.
Model Predictive Contouring Control (MPCC) has shown to achieve very good results in the task of time-optimal multi-waypoint flight. MPCC methods have the freedom to select the optimal states of the system at runtime, dropping the need for a computationally expensive reference trajectory. Our recent work shows MPCC can achieve better lap times than state-of-the-art planning+tracking approaches, and that the method can be run in real-time.
Model Predictive Contouring Control (MPCC) has shown to achieve very good results in the task of time-optimal multi-waypoint flight. MPCC methods have the freedom to select the optimal states of the system at runtime, dropping the need for a computationally expensive reference trajectory. Our recent work shows MPCC can achieve better lap times than state-of-the-art planning+tracking approaches, and that the method can be run in real-time.
One of the extra benefits of the MPCC approach is that there are only two relevant parameters to be tuned in the cost function: contour weight and progress weight. In this project, we aim to exploit the low dimensionality of this tuning parameter space and apply learning techniques to find a mapping from a high-level task (track waypoints in a certain order in minimum time, for example) to MPCC tuning parameters.
One of the extra benefits of the MPCC approach is that there are only two relevant parameters to be tuned in the cost function: contour weight and progress weight. In this project, we aim to exploit the low dimensionality of this tuning parameter space and apply learning techniques to find a mapping from a high-level task (track waypoints in a certain order in minimum time, for example) to MPCC tuning parameters.
Please send your CV and transcripts (bachelor and master) to Angel Romero (roagui AT ifi DOT uzh DOT ch) and Yunlong Song (song AT ifi DOT uzh DOT ch)
Please send your CV and transcripts (bachelor and master) to Angel Romero (roagui AT ifi DOT uzh DOT ch) and Yunlong Song (song AT ifi DOT uzh DOT ch)