S
Swaroop Darbha
Researcher at Texas A&M University
Publications - 173
Citations - 4338
Swaroop Darbha is an academic researcher from Texas A&M University. The author has contributed to research in topics: Travelling salesman problem & Approximation algorithm. The author has an hindex of 28, co-authored 162 publications receiving 3767 citations. Previous affiliations of Swaroop Darbha include Air Force Research Laboratory & University of California, Berkeley.
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Proceedings ArticleDOI
A method for estimating non-responsive traffic at a router
TL;DR: The idea of the proposed scheme is that if the observed queue length and packet drop probability do not match with the predicted results from the TCP model, then the error must come from the non-responsive traffic; it can then be used for estimating non- responsive traffic.
Proceedings ArticleDOI
An Approximation Algorithm for an Assisted Shortest Path Problem
Christopher Montez,Sivakumar Rathinam,Swaroop Darbha,David W. Casbeer,Satyanarayana G. Manyam +4 more
TL;DR: In this article, a cooperative path planning algorithm for a cardinal and a support robot where the cardinal robot is unable to traverse a subset of edges in a network until the support robot has first traversed them is presented.
Journal ArticleDOI
Identification and estimation of parameters defining a class of hybrid systems
TL;DR: In this article, the authors consider the problem of parameter estimation in an air brake system, where the clearance between the brake pads and the drum can vary due to a variety of factors, such as brake pad wear or brake fade.
Proceedings ArticleDOI
Estimation of Pushrod Stroke in an Air Brake System with Parametric Uncertainty
Posted Content
Bounds on Optimal Revisit Times in Persistent Monitoring Missions with a Distinct \& Remote Service Station.
Sai Krishna Kanth Hari,Sivakumar Rathinam,Swaroop Darbha,Krishnamoorthy Kalyanam,Satyanarayana G. Manyam,David W. Casbeer +5 more
TL;DR: In this paper, the authors consider the case in which the service station is not co-located with any of the targets and develop an algorithm to construct near-optimal solutions to the problem quickly, when the fuel capacity exceeds a threshold.