Research

Communication As A Sensor

 

Multi-robot Sequential Decision Making

Multi-robot Sequential Decision Making

 

Resilience in Multi-robot Coordination

Crowd Vetting

 

Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems (to appear CoRL 2020)

Simulation video

 

Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi (to appear, Robotics (cs.RO))

Latest research on switching topologies for rejecting spoofed nodes in multi-agent consensus based on observations over the Wi-Fi communication channels (to appear, ICRA 2019)

Play video on switched topologies for resilient consensus using wifi signals
Switched topologies for resilient consensus using wifi signals

Stephanie Gil's research on adaptive communication networks

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Adaptive communication using only channel feedback

Multi-vehicle network on YouTube
Adaptive communication demonstrates client tradeoff due to obstacle presence

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Adaptive communication demonstrates client tradeoff due to obstacle presence (Directors cut! More polished version above)

Publications

Ninad Jadhav, Weiying Wang, Diana Zhang, Swarun Kumar, and Stephanie Gil. 9/24/2021. “Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics.” arXiv preprint arXiv:2109.12205. Publisher's VersionAbstract
This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.
Ninad Jadhav, Weiying Wang, Diana Zhang, Oussama Khatib, Swarun Kumar, and Stephanie Gil. 2020. “WSR: A WiFi Sensor for Collaborative Robotics”. Publisher's VersionAbstract
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.
Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi
Weiying Wang, Ninad Jadhav, Paul Vohs, Nathan Hughes, Mark Mazumder, and Stephanie Gil. 12/29/2019. “Active Rendezvous for Multi-Robot Pose Graph Optimization using Sensing over Wi-Fi.” In International Symposium on Robotics Research (ISRR). Hanoi.Abstract
We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that ad- dresses two important challenges for multi-robot SLAM: i) that of en- abling information exchange “on-demand” via Active Rendezvous without using a map or the robot’s location, and ii) that of rejecting outlying mea- surements. Our key insight is to exploit relative position data present in the communication channel between robots to improve groundtruth accu- racy of PGO. We develop an algorithmic and experimental framework for integrating Channel State Information (CSI) with multi-robot PGO; it is distributed, and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots and observe that using Active Rendezvous re- sults in a 64% reduction in ground truth pose error and that using CSI observations to aid outlier rejection reduces ground truth pose error by 32%. These results show the potential of integrating communication as a novel sensor for SLAM.
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