UAV control
In this paper, we investigate the problem of enabling a drone to
fly through a tilted narrow gap, without a traditional planning
and control pipeline. To this end, we propose an end-to-end
policy network, which imitates from the traditional pipeline and
is fine-tuned using reinforcement learning. Unlike previous
works which plan dynamical feasible trajectories using motion
primitives and track the generated trajectory by a geometric
controller, our proposed method is an end-to-end approach which
takes the flight scenario as input and directly outputs
thrust-attitude control commands for the quadrotor. Key
contributions of our paper are: 1) presenting an
imitate-reinforce training framework. 2) flying through a narrow
gap using an end-to-end policy network, showing that learning
based method can also address the highly dynamic control problem
as the traditional pipeline does (see attached video
https://www.youtube.com/watch?v=-HXARYlhat4
). 3) propose a robust imitation of an optimal trajectory
generator using multilayer perceptrons. 4) show how
reinforcement learning can improve the performance of imitation
learning, and the potential to achieve higher performance over
the model-based method.
