@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}
}