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.

中文翻译：

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四旋翼垂直壁上侵略性栖息地的强化学习轨迹生成和控制

微型飞行器由于其相对较低的运行成本和在各种应用中的高灵活性而被广泛研究和采用。我们研究了驱动不足的四旋翼飞行器栖息问题，设计了一个轨迹规划器和控制器，该规划器和控制器生成可行的轨迹并将四旋翼飞行器驱动到状态空间中的所需状态。提出了一种利用强化学习控制器和传统控制器优势的四旋翼飞行器轨迹生成与跟踪方法。训练有素的低级增强学习控制器将在仿真环境中将四旋翼操纵到栖息点。一旦成功模拟了四旋翼飞行器，就将模拟中的相对轨迹信息发送到实际四旋翼飞行器的跟踪控制器，并开始实际的栖息任务。通过训练有素的强化学习控制器生成可行的轨迹需要更少的时间，并且传统的轨迹跟踪控制器可以轻松地修改为控制四旋翼并对其稳定性和鲁棒性进行数学分析。我们证明了这种方法允许轨迹和控制器的控制结构，使这种激进的机动以很高的精度栖息在垂直表面上。