Speaker:
Michael Bode
Senior Robotics Engineer
Robotics Engineering Center
Abstract:
A forward predictive model is used to simulate a vehicle's motion given a sequence of commands that could potentially be executed. Generally, forward predictive models are used by planning systems on Unmanned Ground Vehicles (UGV's) for selecting commands such that progress is made and obstacles are avoided. In this talk, I will present a data-driven approach for learning a forward predictive model based on previous vehicle experience. Results of this approach will be presented and will be compared to the conventional model that is currently used on the Crusher vehicle. In addition, performance will be analyzed from the recent Ft. Carson (August 2007) field test where a learned forward predictive model was used to traverse 100 km of off-road terrain autonomously.
Speaker bio:
Michael is currently a member of the autonomy team for the UPI project at the NREC. His research interests lie in perception, planning and large autonomous systems. He has been with the NREC since 2000 and has also worked on LAGR, Perceptor, Underground Mining Operator Assist and the Servus Retail Robot projects. Prior to coming to the NREC, Michael worked with the Robot Learning Lab as an undergraduate. He received his B.S. in Computer Science from Carnegie Mellon University in 2000 with a minor in Robotics and will be completing his M.S. in Robotics this fall.
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