WSR: A WiFi Sensor for Collaborative Robotics
WSR: A WiFi Sensor for Collaborative Robotics
复制标题
DOI：
--
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Ninad Jadhav;Weiying Wang;Diana Zhang;O. Khatib;Swarun Kumar;Stephanie Gil
中科院分区：
文献类型：
--
作者：
Ninad Jadhav;Weiying Wang;Diana Zhang;O. Khatib;Swarun Kumar;Stephanie Gil
在本文中，我们为机器人提供了一种新的能力，可以在不需要外部基础架构的情况下测量其他机器人，以衡量相对方向或到达角度（AOA）（AOA）（AOA）。我们这样做是通过捕获WiFi信号从传输到接收机器人的所有路径遍历的所有路径，我们将其称为AOA轮廓。关键直觉是在机器人在3D空间中移动时“模拟空气中的天线阵列”，这种方法类似于合成孔径雷达（SAR）。主要贡献包括开发i）一个可容纳任意3D轨迹的框架，以及连续的所有机器人，同时计算AOA配置文件和ii）随附的分析，该分析在AOA估计方面提供了较低的AOA估计，作为机器人轨迹的函数基于Cramer Rao结合的几何形状。这是对以前在SAR上的工作的关键区别，将机器人移动性限制为规定的运动模式，不会推广到3D空间，并且/或要求在数据采集期间传输机器人是静态的。我们的方法导致更准确的AOA曲线，从而更好地估计AOA，并正式将这种观察结果表征为轨迹的信息。我们得出封闭形式的可计算数量。所有理论发展都通过广泛的模拟和硬件实验来证实。我们还表明，我们的公式可以与现成的轨迹估计传感器一起使用。最后，我们在多机器人动态聚会任务上演示了系统的性能。
In this paper we derive a new capability for robots to measure relative direction, or Angle-of-Arrival (AOA), to other robots operating in non-line-of-sight and unmapped environments with occlusions, without requiring external infrastructure. We do so by capturing all of the paths that a WiFi signal traverses as it travels from a transmitting to a receiving robot, which we term an AOA profile. The key intuition is to "emulate antenna arrays in the air" as the robots move in 3D space, a method akin to Synthetic Aperture Radar (SAR). The main contributions include development of i) a framework to accommodate arbitrary 3D trajectories, as well as continuous mobility all robots, while computing AOA profiles and ii) an accompanying analysis that provides a lower bound on variance of AOA estimation as a function of robot trajectory geometry based on the Cramer Rao Bound. This is a critical distinction with previous work on SAR that restricts robot mobility to prescribed motion patterns, does not generalize to 3D space, and/or requires transmitting robots to be static during data acquisition periods. Our method results in more accurate AOA profiles and thus better AOA estimation, and formally characterizes this observation as the informativeness of the trajectory; a computable quantity for which we derive a closed form. All theoretical developments are substantiated by extensive simulation and hardware experiments. We also show that our formulation can be used with an off-the-shelf trajectory estimation sensor. Finally, we demonstrate the performance of our system on a multi-robot dynamic rendezvous task.