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arXiv 2012.04174cs.ROcs.MA

一种基于无线信号的机器人感知框架

A wireless signal-based sensing framework for robotics

Harvard University(哈佛大学);Carnegie Mellon University(卡内基梅隆大学);Stanford University(斯坦福大学)

Ninad Jadhav, Weiying Wang, Diana Zhang, Oussama Khatib, Swarun Kumar, Stephanie Gil

AI总结:

提出一种利用机器人移动性模拟天线阵列的无线信号感知框架,通过SAR方法计算AOA轮廓,实现非视距环境下的方向估计,3D运动显著提升精度。

摘要:

在本文中,我们开发了一种新颖的基于无线信号的机器人感知能力(WSR)的分析框架,该框架利用机器人的移动性。它允许机器人在非视距、未映射的环境中,且无需外部基础设施的情况下,主要测量相对于其他机器人的方向,即到达角(AOA)。我们通过捕获无线信号在团队中从发射机器人到接收机器人传播时所经过的所有路径来实现这一点,我们将其称为AOA轮廓。我们方法的关键直觉是使机器人在2D和3D空间中自由移动时能够模拟天线阵列。因此,无线信号相位的微小差异结合机器人局部位移的知识被处理以获得该轮廓,采用类似于合成孔径雷达(SAR)的方法。这项工作的主要贡献是开发了:i)一个框架,以适应任意2D和3D运动,以及信号发射和接收机器人的连续移动性,同时计算它们之间的AOA轮廓;ii)基于天线阵列理论的Cramer-Rao界分析,该分析提供了AOA估计方差的下界,作为机器人运动几何形状的函数。我们表明,允许机器人在执行SAR时充分利用其在3D空间中的移动性,会导致更准确的AOA轮廓,从而获得更好的AOA估计。所有分析发展都通过使用5 GHz WiFi在空中/地面机器人平台上的广泛仿真和硬件实验得到了证实。我们的实验结果支持了我们的分析发现,表明3D运动提供了增强且一致的准确性,在95%的试验中总AOA误差小于10度。我们还分析性地表征了位移估计误差对测量的AOA的影响。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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