Abstract In tasks such as surveying or monitoring remote regions, an autonomous robot must move while transmitting data over a wireless network with unknown, position-dependent transmission rates. For such a robot, this paper considers the problem of transmitting a data buffer in minimum time, while possibly also navigating towards a goal position. Two approaches are proposed, each consisting of a machine-learning component that estimates the rate function from samples; and of an optimal-control component that moves the robot given the current rate function estimate. Simple obstacle avoidance is performed for the case without a goal position. In extensive simulations, these methods achieve competitive performance compared to known-rate and unknown-rate baselines. A real indoor experiment is provided in which a Parrot AR.Drone 2 successfully learns to transmit the buffer.

中文翻译：

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未知通信速率下移动机器人传输导航的学习控制

摘要 在诸如勘测或监测偏远地区之类的任务中，自主机器人必须移动，同时通过无线网络以未知的、与位置相关的传输速率传输数据。对于这样的机器人，本文考虑了在最短的时间内传输数据缓冲区的问题，同时还可能导航到目标位置。提出了两种方法，每种方法都包含一个机器学习组件，可以从样本中估计速率函数；以及在给定当前速率函数估计的情况下移动机器人的最优控制组件。对于没有目标位置的情况执行简单的避障。在广泛的模拟中，与已知速率和未知速率基线相比，这些方法实现了具有竞争力的性能。提供了一个真正的室内实验，其中 Parrot AR。