@article{Tardioli2018,
title = {Pound: A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks},
author = {Danilo Tardioli and Ramviyas Parasuraman and Petter Ögren. },
url = {https://www.sciencedirect.com/science/article/pii/S0921889017309144},
doi = {10.1016/j.robot.2018.10.009},
year = {2019},
date = {2019-01-01},
journal = {Robotics and Autonomous Systems},
volume = {111},
pages = {73-87},
abstract = {The Robot Operating System (ROS) is a popular and widely used software framework for building robotics systems. With the growth of its popularity, it has started to be used in multi-robot systems as well. However, the TCP connections that the platform relies on for connecting the so-called ROS nodes presents several issues regarding limited-bandwidth, delays, and jitter, when used in wireless multi-hop networks. In this paper, we present a thorough analysis of the problem and propose a new ROS node called Pound to improve the wireless communication performance by reducing delay and jitter in data exchanges, especially in multi-hop networks. Pound allows the use of multiple ROS masters (roscores), features data compression, and importantly, introduces a priority scheme that allows favoring more important flows over less important ones. We compare Pound to the state-of-the-art solutions through extensive experiments and show that it performs equally well, or better in all the test cases, including a control-over-network example.},
keywords = {multi-robot, networking, robotics},
pubstate = {published},
tppubtype = {article}
}

@article{Haseeb2018,
title = {Wisture: Touch-less Hand Gesture Classification in Unmodified Smartphones Using Wi-Fi Signals},
author = {Mohamed Haseeb and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/8493572},
doi = {10.1109/JSEN.2018.2876448},
year = {2019},
date = {2019-01-01},
journal = { IEEE Sensors Journal},
volume = {19},
number = {1},
pages = {257-267},
abstract = {This paper introduces Wisture, a new online machine learning solution for recognizing touch-less dynamic hand gestures on a smartphone. Wisture relies on the standard Wi-Fi Received Signal Strength (RSS) using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), thresholding filters and traffic induction. Unlike other Wi-Fi based gesture recognition methods, the proposed method does not require a modification of the smartphone hardware or the operating system, and performs the gesture recognition without interfering with the normal operation of other smartphone applications. We discuss the characteristics of Wisture, and conduct extensive experiments to compare its performance against state-of-the-art machine learning solutions in terms of both accuracy and time efficiency. The experiments include a set of different scenarios in terms of both spatial setup and traffic between the smartphone and Wi-Fi access points (AP). The results show that Wisture achieves an online recognition accuracy of up to 94% (average 78%) in detecting and classifying three hand gestures.},
keywords = {networking, perception, robotics},
pubstate = {published},
tppubtype = {article}
}

@conference{Yang2019,
title = {Self-Reactive Planning of Multi-Robots with Dynamic Task Assignments},
author = {Qin Yang and Zhiwei Luo and Wenzhan Song and Ramviyas Parasuraman},
year = {2019},
date = {2019-08-22},
booktitle = {Int. Symp. on Multi Robot Systems (MRS)},
organization = {Rutgers, NJ, USA},
abstract = {Multi-Robot Systems and Swarms are intelligent systems in which a large number of agents are coordinated in a distributed and decentralized way. Each robot may have homogeneous or heterogeneous capabilities and can be programmed with several fundamental control laws adapting to the environment. Through different kinds of relationships built using the communication protocols, they present various behaviors based on the shared information and current state. To adapt to dynamic environments effectively, and maximize the utility of the group, robots need to cooperate, share their local information, and make a suitable plan according to the specific scenario. In this paper, we formalize the problem of multi-robots fulfilling dynamic tasks using state transitions represented through a Behavior Tree. We design a framework with corresponding distributed algorithms for communications between robots and negotiation and agreement protocols through a novel priority mechanism. Finally, we evaluate our framework through simulation experiments.},
keywords = {cooperation, multi-robot systems, planning},
pubstate = {published},
tppubtype = {conference}
}

@article{Parasuraman2018c,
title = {Special Issue on “Assistive Robotics”},
author = {Ramviyas Parasuraman and Byung-Cheol Min},
url = {https://www.mdpi.com/2227-7080/6/4/95},
doi = {10.3390/technologies6040095},
year = {2018},
date = {2018-10-20},
abstract = {The technology behind robotics has rapidly advanced to a level enabling humans and robots to interact in everyday aspects of life. Nevertheless, it remains a challenge to design and develop these interactions to accommodate people of varying abilities [1]. Assistive Robotics is a branch of robotics that addresses the research challenges inherent in providing sensory and perception abilities and performing actions that are beneficial to the elderly and physically-challenged people [2,3]. This Special Issue presents recent research advances in the field of Assistive Robotics that can empower people to perform various tasks they could not otherwise, to be more independent, and to improve their overall quality of life. Robots for the visually impaired, telepresence robots for physical impairments, social robots for cognitive impairments, and wearable robots are some of the areas of research that were welcomed in this special issue.},
keywords = {assistive devices, robotics},
pubstate = {published},
tppubtype = {article}
}

@conference{Parasuraman2018,
title = {Consensus Control of Distributed Robots Using Direction of Arrival of Wireless Signals},
author = {Ramviyas Parasuraman and Byung-Cheol Min.},
url = {https://www.youtube.com/watch?v=6BkFrJ8vceg&feature=youtu.be},
year = {2018},
date = {2018-10-15},
abstract = {In multi-robot applications, consensus control and coordination are vital and potentially repetitive tasks. To circumvent practical limitations such as a global localization system, researchers have focused on bearing-based consensus controllers, but most assumed that measurements from sensors (e.g. vision) are noise-free. In this paper, we propose to use wireless signal measurements to estimate the direction of arrival (relative bearings) of neighboring robots and introduce a weighted bearing consensus controller to achieve coordinate-free distributed multi-robot rendezvous. We prove that the proposed controller guarantees connectivity maintenance and convergence even in the presence of measurement noise. We conduct extensive numerical simulation experiments using the Robotarium multi-robot platform to verify and demonstrate the properties of the proposed controller and to compare the performance of the rendezvous task against several state-of-the-art rendezvous controllers.},
keywords = {control, multi-robot, networking, robotics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Parasuraman2018d,
title = {Kalman filter based spatial prediction of wireless connectivity for autonomous robots and connected vehicles},
author = {Ramviyas Parasuraman, Petter Ögren, Byung-Cheol Min},
url = {https://www.researchgate.net/publication/326235283_Kalman_Filter_Based_Spatial_Prediction_of_Wireless_Connectivity_for_Autonomous_Robots_and_Connected_Vehicles},
year = {2018},
date = {2018-08-27},
abstract = {This paper proposes a new Kalman filter based online framework to estimate the spatial wireless connectivity in terms of received signal strength (RSS), which is composed of path loss and the shadow fading variance of a wireless channel in autonomous vehicles. The path loss is estimated using a localized least squares method and the shadowing effect is predicted with an empirical (exponential) variogram. A discrete Kalman Filter is used to fuse these two models into a statespace formulation. The approach is unique in a sense that it is online and does not require the exact source location to be known apriori. We evaluated the method using real-world measurements dataset from both indoors and outdoor environments. The results show significant performance improvements compared to state-of-the-art methods using Gaussian processes or Kriging interpolation algorithms. We are able to achieve a mean prediction accuracy of up to 96% for predicting RSS as far as 20 meters ahead in the robot s trajectory.},
keywords = {networking, robotics},
pubstate = {published},
tppubtype = {conference}
}