Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning
Tethered Multicopter Guidance in GPS-Denied Environments Through Reinforcement Learning
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DOI：
10.2514/6.2023-0507
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Amer Al-Radaideh;Robert Selje;Daniel Coraspe;Efe Camci;R. Dutta;Liang Sun;Senthilnath Jayavelu;Xiaoli Li
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文献类型：
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作者：
Amer Al-Radaideh;Robert Selje;Daniel Coraspe;Efe Camci;R. Dutta;Liang Sun;Senthilnath Jayavelu;Xiaoli Li
本文提出了一种新颖的强化学习（RL）方法，用于在无GPS环境下使系留无人机遵循预定的三维轨迹。所采用的Q学习策略利用来自机载加速度计、陀螺仪和高度计的原始观测值来确定高层动作，这有助于一个低层的比例 - 积分 - 微分（PID）控制器驱动无人机通过参考轨迹上的期望航路点。所提方法的有效性在模拟环境中得到了验证。
: This paper presents a novel reinforcement learning (RL) approach for a tethered drone to follow a predefined three-dimensional trajectory in a GPS-denied environment. The adopted Q-learning strategy determines high-level actions using raw observations from the onoard accelerometers, gyros, and altimeter, which facilitates a low-level proportional-integral-derivative (PID) controller to drive the drone through the desired waypoints on a reference trajectory. The effectiveness of the proposed approach is demonstrated in a simulated environment.