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Probabilistic System Identification of a Quadrotor Platform
This work investigates the usage of Gaussian Processes for uncertainty-aware system identification of a quadrotor platform.
Most planning & control algorithms used on quadrotors make use of a nominal model of the platform dynamics to compute feasible trajectories or generate control commands. Such models are derived using first principles and typically cannot fully capture the true dynamics of the system, leading to sub-optimal performance. One appealing approach to overcome this limitation is to use Gaussian Processes for system modeling. Gaussian Process regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in the prediction.
This work investigates the usage of Gaussian Processes for uncertainty-aware system identification of a quadrotor platform.
Requirements:
- Machine learning experience preferable but not strictly required
- Programming experience in C++ and Python
Most planning & control algorithms used on quadrotors make use of a nominal model of the platform dynamics to compute feasible trajectories or generate control commands. Such models are derived using first principles and typically cannot fully capture the true dynamics of the system, leading to sub-optimal performance. One appealing approach to overcome this limitation is to use Gaussian Processes for system modeling. Gaussian Process regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in the prediction.
This work investigates the usage of Gaussian Processes for uncertainty-aware system identification of a quadrotor platform.
Requirements:
- Machine learning experience preferable but not strictly required
- Programming experience in C++ and Python
Implement an uncertainty-aware model of the quadrotor dynamics, train and evaluate the model on simulated and real data.
Implement an uncertainty-aware model of the quadrotor dynamics, train and evaluate the model on simulated and real data.
