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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Journal ArticleDOI
Minimal Energy Routing of a Leader and a Wingmate with Periodic Connectivity
TL;DR: In this article , the authors consider a special case of the problem where equal weights are assigned to the distances traveled by the vehicles and the communicating signals and show that the approximation algorithm has a fixed approximation ratio of 3.75.
Posted Content
Benefits of V2V Communication for Autonomous and Connected Vehicles
TL;DR: The benefits of using V2V communication for autonomous vehicles are quantified in terms of a reduction in the employable time headway.
Reconfiguration of a vehicle formation with ring communication structure
TL;DR: It is shown that the directed ring graph is well suited for adding vehicles from the point of view of scalability of the existing controller and the ease with which the existing ring structure will be able to handle the increase in the formation size.
Proceedings ArticleDOI
Multi-Agent Task Assignment and Sequencing using Monte Carlo Tree Search and Process Algebra
Steven Rasmussen,David W. Casbeer,Abhaysingh Bhadoriya,Swaroop Darbha,Satyanarayana G. Manyam +4 more
TL;DR: In this article , the authors explore the advantages and disadvantages of solving multi-agent to task assignment problems by searching state-space trees using Monte Carlo Tree Search (MCTS) and Process Algebra (PA).
Journal ArticleDOI
Performance Guarantee of a Sub-Optimal Policy for a Robotic Surveillance Application*
Myoungkuk Park,Krishnamoorthy Kalyanam,Swaroop Darbha,Pramod P. Khargonekar,Phillip Chandler,Meir Pachter +5 more
TL;DR: The novel feature of this paper is to present a lower bound via LP based techniques and state partitioning and construct a sub-optimal policy whose performance betters the lower bound.