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.

翻译：在本文中,我们为机器人提供了一种新的能力,以测量相对方向,或Angle-Arrive(AOA),以测量在非线性视觉和无绘图环境中运行的其他机器人,而不需要外部基础设施。我们这样做的方法是捕捉WiFi信号穿行从传输到接收机器人(我们称之为AOA剖析图)的所有路径。关键直觉是“模拟天线阵列”,因为机器人在3D空间移动,一种类似于合成孔径雷达(SAR)的方法。主要贡献包括开发一个框架,以适应任意的3D轨迹,以及持续移动所有机器人,同时计算AOAO剖图和二),同时进行一项分析,在根据Cramer Rao Bound 进行机器人轨迹测函数的功能上,对AOA的偏差进行较低约束。我们以前在SAR上的工作限制机器人运动模式的移动性能,并不向3D空间进行概括化的同步轨道。主要贡献包括:i)一个框架,一个容纳任意的3D轨迹的多轨迹,以及一个连续的机器人,因此需要将一个固定的轨道数据转换为我们的数据。