Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal 'bang-bang' control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance & Control Networks by learning to take consecutive waypoints into account. We fly a 4x3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance & Control Networks offer.
@article{arxiv.2305.02705,
title = {Guidance & Control Networks for Time-Optimal Quadcopter Flight},
author = {Sebastien Origer and Christophe De Wagter and Robin Ferede and Guido C. H. E. de Croon and Dario Izzo},
journal= {arXiv preprint arXiv:2305.02705},
year = {2023}
}