Learning the Forward Predictive Model for an Off-Road Skid-Steer Vehicle
Tech. Report, CMU-RI-TR-07-32, Robotics Institute, Carnegie Mellon University, September, 2007
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 for Unmanned Ground Vehicles (UGV's) so that commands can be selected such that obstacles are avoided. This report presents a data- driven approach for learning a forward predictive model based on previously recorded vehicle motion. The selected approach is compared to several variations including the conventional forward predictive model that has traditionally been used on the Crusher vehicle. Results are presented using real life data collected on the Crusher UGV.
BibTeX
@techreport{Bode-2007-9818,author = {Michael W. Bode},
title = {Learning the Forward Predictive Model for an Off-Road Skid-Steer Vehicle},
year = {2007},
month = {September},
institute = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-07-32},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.