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Vision-Based MPC Control
Tightly coupled integration of vision for MPC
Keywords: Neural Network Control, Model Predictive Control, Drones, UAV, Vision Based Agile Flight,
Model predictive control (MPC) is a versatile optimization-based control method that allows the incorporation of constraints directly into the control problem. The advantages of MPC can be seen in its ability to accurately control dynamic systems that include large time delays and high-order dynamics. Recent advances in compute hardware allow running MPC even on compute-constrained quadrotors. While model-predictive control can deal with complex systems and constraints, it still assumes the existence of a reference trajectory. With this project, we aim to find a tight coupling of perception and control would allow pushing the speed limits of autonomous flight through cluttered environments.
Requirements:
Machine learning experience (TensorFlow and/or PyTorch), Experience in MPC preferable but not strictly required, Programming experience in C++ and Python
Model predictive control (MPC) is a versatile optimization-based control method that allows the incorporation of constraints directly into the control problem. The advantages of MPC can be seen in its ability to accurately control dynamic systems that include large time delays and high-order dynamics. Recent advances in compute hardware allow running MPC even on compute-constrained quadrotors. While model-predictive control can deal with complex systems and constraints, it still assumes the existence of a reference trajectory. With this project, we aim to find a tight coupling of perception and control would allow pushing the speed limits of autonomous flight through cluttered environments.
Requirements: Machine learning experience (TensorFlow and/or PyTorch), Experience in MPC preferable but not strictly required, Programming experience in C++ and Python
Implement the learned perception system in simulation and integrate the predictions into an existing MPC pipeline. If possible, deploy on a real system.
Implement the learned perception system in simulation and integrate the predictions into an existing MPC pipeline. If possible, deploy on a real system.
Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Drew Hanvoer (hanover (at) ifi (dot) uzh (dot) ch)
Leonard Bauersfeld (bauersfeld AT ifi DOT uzh DOT ch), Drew Hanvoer (hanover (at) ifi (dot) uzh (dot) ch)