Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its safety and intended behavior: in particular actuator and system failures and external disturbances are among the most common causes of degraded mode of operation. To deal with this problem, in this work, we present a meta-learning-based approach to improve the trajectory tracking performance of an unmanned aerial vehicle (UAV) under actuator faults and disturbances which have not been previously experienced. Our approach leverages meta-learning to adapt the system's model at runtime to make accurate predictions about the system's future state. A runtime monitoring and validation technique is proposed to decide when the system needs to adapt its model by considering a data pruning procedure for efficient learning. Finally the desired trajectory is adapted based on future predictions by borrowing robust control logic to make the system track the original and desired path without needing to access the system's controller. The proposed framework is applied and validated in both simulations and experiments on a faulty UAV navigation case study demonstrating a drastic increase in tracking performance.

中文翻译：

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退化条件下基于元学习的无人机航迹跟踪框架

由于模型动力学的变化或意外干扰，自主机器人系统在实际操作过程中可能会遇到无法预料的挑战，这可能会影响其安全性和预期行为：特别是执行器和系统故障以及外部干扰是降级模式的最常见原因之一操作。为了解决这个问题，在这项工作中，我们提出了一种基于元学习的方法，以改善执行器故障和干扰下无人驾驶飞机（UAV）的轨迹跟踪性能，这是以前从未经历过的。我们的方法利用元学习在运行时调整系统模型，从而对系统的未来状态做出准确的预测。提出了一种运行时监视和验证技术，通过考虑数据修剪过程以进行有效学习来决定系统何时需要调整其模型。最终，通过借鉴鲁棒的控制逻辑，基于未来的预测调整所需的轨迹，使系统无需访问系统的控制器即可跟踪原始路径和所需路径。所提出的框架在错误的无人机导航案例研究中的仿真和实验中均得到了应用和验证，这证明了跟踪性能的显着提高。