The capability to autonomously track a non-cooperative target is a key technological requirement for micro aerial vehicles. In this paper, we propose an output feedback control scheme based on deep reinforcement learning for controlling a micro aerial vehicle to persistently track a flying target while maintaining visual contact. The proposed method leverages relative position data for control, relaxing the assumption of having access to full state information which is typical of related approaches in literature. Moreover, we exploit classical robustness indicators in the learning process through domain randomization to increase the robustness of the learned policy. Experimental results validate the proposed approach for target tracking, demonstrating high performance and robustness with respect to mass mismatches and control delays. The resulting nonlinear controller significantly outperforms a standard model-based design in numerous off-nominal scenarios.

翻译：自主追踪非合作目标是微型无人机的一项关键技术需求。本文提出一种基于深度强化学习的输出反馈控制方案，用于控制微型无人机在保持视觉接触的同时持续追踪空中目标。该方法利用相对位置数据进行控制，放松了现有文献中相关方法通常需要的全状态信息假设。此外，我们通过域随机化方法将经典鲁棒性指标引入学习过程，以提升所学策略的鲁棒性。实验结果验证了所提方法在目标追踪中的有效性，表明该方法在质量失配和控制延迟方面具有高性能和鲁棒性。所获得的非线性控制器在众多非标称场景中显著优于基于模型的标准设计方法。