A wireless signal-based sensing framework for robotics
A wireless signal-based sensing framework for robotics
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DOI：
10.1177/02783649221097989
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发表时间：
2020-12
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影响因子：
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通讯作者：
Ninad Jadhav;Weiying Wang;Diana Zhang;O. Khatib;Swarun Kumar;Stephanie Gil
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作者：
Ninad Jadhav;Weiying Wang;Diana Zhang;O. Khatib;Swarun Kumar;Stephanie Gil
在本文中，我们开发了一种新型的基于无线信号的机器人感知能力(WSR)的分析框架，利用机器人在3D空间中的移动性。它允许机器人主要测量相对于其他机器人的相对方向或到达角(AOA)，同时在非视线未映射环境中操作，并且不需要外部基础设施。我们通过捕获无线信号从发射机器人传输到团队中的接收机器人时经过的所有路径来实现这一点，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模拟天线阵列，因为它在2D和3D空间中自由移动。因此，通过一种类似于合成孔径雷达(SAR)的方法，利用机器人局部位移的知识来处理无线信号的微小相位差，以获得轮廓。这项工作的主要贡献是发展了(I)一个框架，以适应任意的2D和3D运动，以及信号发送和接收机器人的连续移动性，同时计算它们之间的AOA分布；(Ii)基于天线阵列理论的Cramer-Rao界分析，它提供了作为机器人运动几何函数的AOA估计的方差的下界。这与以前基于合成孔径雷达的方法的关键区别在于，基于合成孔径雷达的方法将机器人的移动性限制在指定的运动模式，不能推广到整个3D空间，并且要求传输机器人在数据采集期间保持静止。我们表明，允许机器人在执行合成孔径雷达时充分利用其在3D空间中的移动性，可以得到更准确的AOA轮廓，从而获得更好的AOA估计。我们正式地将这种观察描述为机器人运动的信息性，这是一个可计算的量，我们为其推导出一个封闭的形式。在使用5 GHz WiFi的空中/地面机器人平台上，广泛的仿真和硬件实验证实了所有的分析开发。我们的实验结果支持了我们的分析结果，表明3D运动提供了增强的和一致的准确性，对于95%的试验，总的AOA值误差小于10◦。我们还分析了位移估计误差对测量的AOA的影响，并使用使用现成的Intel跟踪摄像头T265获得的机器人位移进行了实证验证。最后，我们展示了我们的系统在多机器人任务中的性能，其中不同种类的空中/地面对机器人通过WiFi链路连续测量AOA轮廓，以在无地图的300平方米的遮挡环境中实现动态交会。
In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots’ mobility in 3D space. 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. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. 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. We formally characterize this observation as the informativeness of the robots’ motion, a computable quantity for which we derive a closed form. 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 a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions.