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Alonzo Kelly
Professor, RI
Email:
Office: NSH 3209
Phone: (412) 268-5330
Fax: 412-681-6961
  Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Administrative Assistant: Lynnetta J. Miller
Affiliated Center(s):
 Field Robotics Center (FRC)
 National Robotics Engineering Center (NREC)
 Center for the Foundations of Robotics (CFR)
Personal Homepage

Past Projects [Current Projects]
 
Autonomous Coordinated Motion and Mobility (ACMM)
The ACMM (Autonomous Coordinated Motion and Mobility) project integrates and develops controls for more efficient supervised teleoperation of mobile manipulators.
Autonomous Loading System (ALS)
Developing technologies to improve the productivity and reduce the cost of mass excavation.
Lunar Rover Initiative (LRD)
first interactive space exploration event
Material Transport (AMTS)
NREC developed AMTS, an innovative system for accurately guiding robotic material transport vehicles in industrial settings.
PerceptOR (NRECPerceptOR)
NREC designed, developed and tested an innovative autonomous perception and navigation system for the DARPA PerceptOR program.
Ranger
We are developing a software control system for cross country autonomous vehicles called RANGER, for Real-time Autonomous Navigator with a Geometric Engine.
Tartan Racing
Carnegie Mellon University is teaming with General Motors to compete in the 2007 DARPA Urban Challenge.
Transitional Unmanned Ground Vehicle (TUGV)
Cross Country Navigation
Unmanned Ground Vehicles
Developing autonomous navigation capabilities for mobile robots driving in complex, unstructured outdoor terrain.
Urban Challenge
Vehicle Stability (SPS)
NREC has developed an easy-to-implement solution to an important problem — stability margin estimation as it relates to vehicular rollover and tipover vulnerabilities.
Very Rough Terrain Nonholonomic Trajectory Generation and Motion Planning for Rovers
We are developing rough terrain trajectory generation algorithms for local path planning and optimal regional motion planning methods using a constrained search space.