End-to-end optimal quadcopter control through Supervised Learning
Notebooks
- Data Generation: used to generate the datasets of optimal trajectories
- Network Training: uses the generated datasets to train a G&CNet to learn the optimal state feedback
- Simulation: simulates the quadcopter controlled by a trained G&CNet
- Generate C code: used to convert the G&CNet from pytorch to c code
- Minimum Snap trajectories: used to compute the minimum snap polynomial trajectories and convert it into C code
Note that in order to run the Dataset Generation notebook, AMPL (A Mathematical Programming Language) needs to be installed as well as the NLP solver SNOPT.