论文标题
在未知的通信率下，使用移动机器人进行传输和导航的学习控制
Learning control for transmission and navigation with a mobile robot under unknown communication rates
论文作者
论文摘要
在诸如测量或监视远程区域之类的任务中，自主机器人必须在通过无线网络传输数据的同时移动，该网络具有未知，依赖位置的传输速率。对于这样的机器人，本文考虑了在最短时间内传输数据缓冲区的问题，同时也可能导航到目标位置。提出了两种方法，每种方法由机器学习组件组成，该组件估算了样品的速率函数。并给定当前速率函数估计的最佳控制组件，该组件可移动机器人。对于没有目标位置的情况，对案件进行了简单的避免障碍。在广泛的模拟中，与已知速率和未知速率基线相比，这些方法实现了竞争性能。提供了一个真正的室内实验，其中鹦鹉ar.drone 2成功地学习了传输缓冲液。
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.
