The Integrated Automation for Sustainable Specialty Crops Farming project teams the National Robotics Engineering Center (NREC), the University of Florida, Cornell University and John Deere to bring precision agriculture and autonomous equipment to citrus growers.
Generates plans for polyhedral sheet metal parts.
Stress Tests for Autonomy Architectures (STAA) finds autonomy system safety problems that are unlikely to be discovered by other types of tests.
NREC developed the Sweep Monitoring System (SMS) for training soldiers and demining personnel to use hand-held land mine detectors.
The Aerial Robotic Infrastructure Analyst (ARIA) rapidly creates comprehensive, high-resolution, semantically rich 3D models of infrastructure – an interactive assistant for infrastructure inspection.
We are designing a bipedal robot to be capable of running, walking, jumping, hopping, and generally behaving in a highly dynamic manner.
This project’s goal is to transform the surface-based representations that are naturally derived from sensed data into volumetric representations needed by CAD and BIM.
We are developing a robotic phenotyping systems for phenotyping crops for rapid breeding decisions. This system positions sensors within the canopy for measurements not observable from above or below. Machine learning and computer vision algorithms are then used to generate phenotyping data from the raw sensor data.
We are developing a single heterogeneous human-robot team capable of effectively locating objects of interest (treasure) spread over a complex, previously unknown environment.
A tree inventory system uses vehicle-mounted sensors to automatically count and map the locations of trees in an orchard.
NREC is pioneering research and development of a low power, small, lightweight system for producing accurate 3D maps of tunnels through its Precision Tunnel Mapping program.
Automating the functions of a continuous mining machine and roof bolting units.
The purpose of this project is to develop methods for place matching that are invariant to short- and long-term environmental variations in support of autonomous vehicle localization in GPS-denied situations.
We are developing rough terraintrajectory generation algorithms for local path planning and optimalregional motion planning methods using a constrained search space.
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.