论文标题
机器人技术的基于无线信号的传感框架
A wireless signal-based sensing framework for robotics
论文作者
论文摘要
在本文中，我们通过利用机器人的迁移率来为机器人技术（WSR）的新型无线传感能力（WSR）开发分析框架。它允许机器人主要在非线未覆盖的环境中操作，而无需外部基础架构，主要可以测量其他机器人的相对方向或到达其他机器人。我们这样做是通过捕获无线信号从传输到团队中接收机器人的所有路径遍历的所有路径，我们将其称为AOA配置文件。我们方法背后的关键直觉是使机器人能够在2D和3D空间自由移动时模拟天线阵列。因此，通过类似于合成孔径雷达（SAR）的方法来处理无线信号阶段的小差异。 The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and ii) a Cramer-Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion.我们表明，允许机器人在执行SAR时在3D空间中使用其完整的移动性，从而产生更准确的AOA配置文件，从而更好地估计AOA。使用5 GHz WiFi在空气/接地机器人平台上进行了广泛的模拟和硬件实验，可以证实所有分析开发。我们的实验结果加强了我们的分析结果，表明3D运动提供了增强且一致的精度，而95％的试验的总AOA误差小于10度。我们还通过分析表征位移估计误差对测得的AOA的影响。
In this paper we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging robots' mobility. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots' local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and ii) a Cramer-Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. We show that allowing robots to use their full mobility in 3D space while performing SAR, results in more accurate AOA profiles and thus better AOA estimation. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with total AOA error of less than 10 degree for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA.
