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Micro aerial vehicles are widely being researched and employed due to theirrelative low operation costs and high flexibility in various applications. Westudy the under-actuated quadrotor perching problem, designing a trajectoryplanner and controller which generates feasible trajectories and drivesquadrotors to desired state in state space. This paper proposes a trajectorygenerating and tracking method for quadrotor perching that takes the advantagesof reinforcement learning controller and traditional controller. The trainedlow-level reinforcement learning controller would manipulate quadrotor towardthe perching point in simulation environment. Once the simulated quadrotor hassuccessfully perched, the relative trajectory information in simulation will besent to tracking controller on real quadrotor and start the actual perchingtask. Generating feasible trajectories via the trained reinforcement learningcontroller requires less time, and the traditional trajectory trackingcontroller could easily be modified to control the quadrotor and mathematicallyanalysis its stability and robustness. We show that this approach permits thecontrol structure of trajectories and controllers enabling such aggressivemaneuvers perching on vertical surfaces with high precision.

Reinforcement Learning Trajectory Generation and Control for Aggressive Perching on Vertical Walls with Quadrotors