P
Pratap Tokekar
Researcher at University of Maryland, College Park
Publications - 149
Citations - 2485
Pratap Tokekar is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Computer science & Approximation algorithm. The author has an hindex of 22, co-authored 127 publications receiving 1800 citations. Previous affiliations of Pratap Tokekar include University of Minnesota & University of Pennsylvania.
Papers
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Journal ArticleDOI
Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture
TL;DR: An approximation algorithm for SamplingTSPN is presented, and how to model the UAV planning problem using a metric graph and formulate an orienteering instance to which a known approximation algorithm can be applied is shown.
Proceedings ArticleDOI
Devices, systems, and methods for automated monitoring enabling precision agriculture
Jnaneshwar Das,Gareth Cross,Chao Qu,Anurag Makineni,Pratap Tokekar,Yash Mulgaonkar,Vijay Kumar +6 more
TL;DR: The design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture, and techniques to extract four key data products - plant morphology, canopy volume, leaf area index, and fruit counts - are described.
Proceedings ArticleDOI
Sensor planning for a symbiotic UAV and UGV system for precision agriculture
TL;DR: A method to identify points whose probability of being misclassified is above a threshold is presented and the problem of maximizing the number of such points visited by an UAV subject to its energy budget is studied.
Journal ArticleDOI
Energy-optimal trajectory planning for car-like robots
TL;DR: This paper studies the problem of finding optimal paths and velocity profiles for car-like robots so as to minimize the energy consumed during motion, and uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories.
Proceedings ArticleDOI
Multi-target visual tracking with aerial robots
TL;DR: This work shows that k ≥ 3 robots may not be able to track all n targets while maintaining a constant factor approximation of the optimal quality of tracking at all times, and forms this problem as the weighted version of a combinatorial optimization problem known as the Maximum Group Coverage (MGC) problem.