用强化学习提前预判干扰，让无人机更稳。
RL-based Control of UAS Subject to Significant Disturbance
- 利用触发信号预测干扰，提前干预
- 预测型策略位置偏差减少40%以上
- 适合高干扰环境下的无人机控制
本文提出一种基于强化学习的无人飞行器（UAS）位置与姿态控制框架，针对可能由不确定触发信号引发的重大干扰。该方法学习触发信号与干扰力之间的关系，使系统能在干扰发生前进行预判并主动补偿。我们训练并评估了三种策略：未接触干扰的基准策略、仅在干扰中训练但无触发信号的反应式策略，以及将触发信号作为观测输入且在干扰中训练的预测式策略。仿真结果表明，预测式策略通过主动修正动作显著降低位置偏差，表现优于其他策略。该工作展示了将预测性线索融入强化学习框架以提升无人机性能的潜力。
原文摘要 · Abstract (English)
This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.
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