  Micro aerial vehicles are widely being researched and employed due to their
relative low operation costs and high flexibility in various applications. We
study the under-actuated quadrotor perching problem, designing a trajectory
planner and controller which generates feasible trajectories and drives
quadrotors to desired state in state space. This paper proposes a trajectory
generating and tracking method for quadrotor perching that takes the advantages
of reinforcement learning controller and traditional controller. The trained
low-level reinforcement learning controller would manipulate quadrotor toward
the perching point in simulation environment. Once the simulated quadrotor has
successfully perched, the relative trajectory information in simulation will be
sent to tracking controller on real quadrotor and start the actual perching
task. Generating feasible trajectories via the trained reinforcement learning
controller requires less time, and the traditional trajectory tracking
controller could easily be modified to control the quadrotor and mathematically
analysis its stability and robustness. We show that this approach permits the
control structure of trajectories and controllers enabling such aggressive
maneuvers perching on vertical surfaces with high precision.
