10.5061/DRYAD.9KD51C5H7
Foehn, Philipp
0000-0001-9585-1278
University of Zurich
Romero, Angel
University of Zurich
Scaramuzza, Davide
University of Zurich
Time-Optimal Planning for Quadrotor Waypoint Flight
Dryad
dataset
2021
FOS: Computer and information sciences
European Research Council
https://ror.org/0472cxd90
864042
European Research Council
https://ror.org/0472cxd90
871479
Swiss National Science Foundation
https://ror.org/00yjd3n13
National Centre of Competence in Research Robotics
http://dx.doi.org/10.13039/501100011021
2021-06-29T00:00:00Z
2021-06-29T00:00:00Z
en
https://arxiv.org/abs/2007.06255
https://doi.org/10.5281/zenodo.5036287
24331725 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Quadrotors are amongst the most agile flying robots. However, planning
time-optimal trajectories at the actuation limit through multiple
waypoints remains an open problem. This is crucial for applications such
as inspection, delivery, search and rescue, and drone racing. Early works
used polynomial trajectory formulations, which do not exploit the full
actuator potential due to their inherent smoothness. Recent works
resorted to numerical optimization, but require waypoints to be allocated
as costs or constraints at specific discrete times. However, this
time-allocation is a priori unknown and renders previous works incapable
of producing truly time-optimal trajectories. To generate truly
time-optimal trajectories, we propose a solution to the time allocation
problem while exploiting the full quadrotor's actuator potential. We
achieve this by introducing a formulation of progress along the
trajectory, which enables the simultaneous optimization of the
time-allocation and the trajectory itself. We compare our method against
related approaches and validate it in real-world flights in one of the
world's largest motion-capture systems, where we outperform human
expert drone pilots in a drone-racing task.
This data contains drone flight tracks of two professional human pilots
and a proposed planning approach, flown in a drone racing environment in
one of the worlds largest flight arenas equipped with a VICON motion
capture system.
All data is provided as is and can be evaluated using the included
analysis scripts.