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.
翻译:由于操作成本相对较低,各种应用具有高度灵活性,目前正在广泛研究和使用微型航空飞行器。我们研究未充分活化的二次钻探渗入问题,设计一个轨迹规划器和控制器,产生可行的轨迹,并将四重钻驱动器驱动到国家空间的预期状态。本文建议了一种利用强化学习控制器和传统控制器优势的四重钻渗入轨迹生成和跟踪方法。经过训练的低级强化学习控制器将操纵振荡器到模拟环境中的切入点。模拟二次钻探器一旦成功渗透,模拟中的相对轨迹信息将发送到真实二次钻探器的跟踪控制器,并开始实际的切入任务。通过经过训练的强化学习控制器生成可行的轨道轨迹需要较少时间,传统的轨迹跟踪控制器将很容易修改,以控制二次钻探器和数学分析其稳定性和稳健性。我们表明,这一方法允许对轨迹和控制器进行控制结构,从而能够对垂直表面进行这种振动性操纵。