Carnegie Mellon University
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Stephen T. Nuske
Systems Scientist, RI
Office: NSH 1105
Phone: (412) 268-5901
  Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Ave
Pittsburgh, PA 15213
Administrative Assistant: Connie Franchini
Affiliated Center(s):
 Field Robotics Center (FRC)

Current Projects
Autonomous Vineyard Canopy and Yield Estimation
The research project aims to design and demonstrate new sensor technologies for autonomously gathering crop and canopy size estimates from a vineyard -- expediently, precisely, accurately and at high-resolution -- with the goal to improve vineyard efficiency by enabling producers to measure and manage the principal components of grapevine production on an individual vine basis.
Comprehensive Automation for Specialty Crops (CASC)
CASC is a multi-institutional initiative led by Carnegie Mellon Robotics Institute to comprehensively address the needs of specialty agriculture focusing on apples and horticultural stock.
Riverine Mapping
This project is developing technology to map riverine environments from a low-flying rotorcraft. Challenges include dealing with varying appearance of the river and surrounding canopy, intermittent GPS and a highly constrained payload. We are developing self-supervised algorithms that can segment images from onboard cameras to determine the course of the river ahead, and we are developing devices and methods capable of mapping the shoreline.
UAV/UGV Air-Ground Collaboration
This project is concerned with the development of a distributed estimation system of collaborating UAVs (Unmanned Aerial Vehicle) and AGVs (Autonomous Ground Vehicles) that detect, track and estimate the location of a person, vehicle or object of interest on the ground.
Visual Yield Mapping with Optimal and Generative Sampling Strategies
This research project aims to develop methods to automatically collect visual image data to infer, estimate and forecast crop yields -- producing yield maps with high-resolution, across large scales and with accuracy. To achieve efficiency and accuracy, statistical sampling strategies are designed for human-robot teams that are optimal in the number of samples, location of samples, cost of sampling and accuracy of crop estimates.