[Paper Review] WSR: A WiFi Sensor for Collaborative Robotics.

Ninad Jadhav, Weiying Wang+4 more|arXiv (Cornell University)|Dec 8, 2020

Indoor and Outdoor Localization Technologies72 references4 citations

TL;DR

This paper proposes WSR, a WiFi-based sensor that enables collaborative robots to estimate relative direction (Angle-of-Arrival) in non-line-of-sight and occluded environments without external infrastructure. By leveraging mobile robots' 3D trajectories to emulate synthetic antenna arrays, WSR computes AOA profiles using a framework that generalizes to arbitrary motion and derives a closed-form informativeness metric based on the Cramér-Rao Bound, significantly improving AOA estimation accuracy over prior SAR-based methods.

ABSTRACT

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.

Motivation & Objective

• To enable relative direction estimation (Angle-of-Arrival) between mobile robots in non-line-of-sight and occluded environments without relying on external infrastructure.
• To overcome limitations of prior synthetic aperture radar (SAR)-based methods that constrain robot motion to fixed patterns and require static transmitters.
• To develop a general framework that supports arbitrary 3D robot trajectories and continuous mobility during AOA estimation.
• To formally quantify the informativeness of robot trajectories for AOA estimation using a closed-form expression derived from the Cramér-Rao Bound.
• To validate the method through simulations, hardware experiments, and integration with off-the-shelf trajectory sensors in a dynamic multi-robot rendezvous task.

Proposed method

• The method captures all multipath components of WiFi signals between robots, forming a comprehensive AOA profile that reflects the signal’s spatial propagation characteristics.
• It leverages the 3D motion of robots as a synthetic aperture, emulating a large virtual antenna array in free space through trajectory diversity.
• A mathematical framework is developed to compute AOA profiles from arbitrary 3D trajectories, enabling continuous mobility of both transmitting and receiving robots.
• The Cramér-Rao Bound is applied to derive a theoretical lower bound on AOA estimation variance, which is then used to define a closed-form informativeness metric for robot trajectories.
• The framework is designed to be compatible with off-the-shelf inertial or dead-reckoning trajectory sensors, enabling practical deployment.
• The method is validated through extensive simulations and hardware experiments, including a dynamic multi-robot rendezvous task.

Experimental results

Research questions

• RQ1 Can WiFi signals be used to estimate Angle-of-Arrival between mobile robots in non-line-of-sight and occluded environments without external infrastructure?
• RQ2 How can arbitrary 3D robot trajectories be leveraged to improve AOA estimation accuracy compared to fixed or constrained motion patterns?
• RQ3 What is the theoretical lower bound on AOA estimation variance as a function of trajectory geometry, and how can it be computed in closed form?
• RQ4 How does the informativeness of a robot trajectory—defined as its ability to reduce AOA estimation uncertainty—depend on its 3D spatial configuration?
• RQ5 Can the proposed framework be practically deployed using off-the-shelf trajectory sensors and demonstrate performance in real-world multi-robot tasks?

Key findings

• The proposed WSR framework enables accurate, infrastructure-free AOA estimation in 3D, non-line-of-sight, and occluded environments using only WiFi signals and mobile robot motion.
• By exploiting arbitrary 3D trajectories, WSR achieves significantly better AOA estimation accuracy than prior SAR-based methods that restrict motion or require static transmitters.
• The closed-form informativeness metric derived from the Cramér-Rao Bound quantitatively captures how trajectory geometry influences estimation accuracy, enabling optimal trajectory design.
• Hardware experiments confirm the theoretical predictions, showing consistent improvement in AOA estimation performance across diverse 3D motion patterns.
• The system successfully supports a dynamic multi-robot rendezvous task, demonstrating robustness and practicality in real-world collaborative scenarios.
• Integration with off-the-shelf trajectory sensors is feasible and effective, enabling deployment without specialized hardware.