Carnegie Mellon University
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David Kohanbash
Senior Research Programmer, RI
Email:
Office: NSH 2203
Phone: (412) 268-3856
  Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Affiliated Center(s):
 Field Robotics Center (FRC)
Personal Homepage

Current Projects [Past Projects]
 
Autonomous Off-Road Driving
We are developing autonomous technology for off-road driving in wilderness environments Key developments include perception, planning and control capabilities. This is a joint development with Yamaha Motor Corporation and the CMU Field Robotics Center.
Distributed SensorWebs
The Sensor Web initiative develops and implements wireless technology for distributed sensing and actuation in horticultural enterprises.
Factory Automation
We are developing the next generation of mobile robots for operating in the factory environments. These mobile robots can localize without modifying the factory and navigate any path in the factory, with the ability to replan paths to avoid unexpected obstacles. These new capabilities will increase the throughput of the factories, as well as decrease the time required to deploy (and re-deploy) the robots into the factory.
Hydroponic Automation
We are developing inexpensive robotic approaches towards hydroponic growing, which can increase overall crop yield.
Life in the Atacama
Robotic field investigation will bring new scientific understanding of the Atacama as a habitat for life with distinct analogies to Mars.
Lunar Ice Discovery Initiative (Icebreaker)
Icebreaker is a proposed mission to explore the south pole of the Moon.
Lunar Rover for Polar Crater Exploration (Scarab)
The Scarab lunar rover has been designed to carry a 1-meter coring drill and a payload of science instruments that can analyze the abundance of hydrogen, oxygen and other materials.
Transportation Energy Resources from Renewable Agriculture (TERRA)
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.
Vehicle Safeguarding
NREC designed, developed and tested a fully autonomous system capable of following pre-taught paths while detecting and avoiding obstacles.