Paper Info
Title
Slip estimation for small-scale robotic tracked vehicles
Abstract
A method is presented for using an extended Kalman filter with state noise compensation to estimate the trajectory, orientation, and slip variables for a small-scale robotic tracked vehicle. The principal goal of the method is to enable terrain property estimation. The methodology requires kinematic and dynamic models for skid-steering, as well as tractive force models parameterized by key soil parameters. Simulation studies initially used to verify the model basis are described, and results presented from application of the estimation method to both simulated and experimental study of a 60-kg robotic tracked vehicle. Preliminary results show the method can effectively estimate vehicle trajectory relying only on the model-based estimation and onboard sensor information. Estimates of slip on the left and right track as well as slip angle are essential for ongoing work in vehicle-based soil parameter estimation. The favorable comparison against motion capture data suggests this approach will be useful for laboratory and field-based application.
Year
DOI
Venue
2010
10.1109/ACC.2010.5531638
American Control Conference
Keywords
DocType
ISSN
kalman filters,compensation,mobile robots,parameter estimation,position control,robot dynamics,robot kinematics,extended kalman filter,model-based estimation,onboard sensor information,skid-steering,slip estimation,small-scale robotic tracked vehicles,state noise compensation,terrain property estimation,tractive force models,vehicle trajectory estimation,vehicle-based soil parameter estimation,estimation,trajectory,tracking,force,mathematical model
Conference
0743-1619
ISBN
Citations 
PageRank 
978-1-4244-7426-4
5
0.57
References 
Authors
2
2
Name
Order
Citations
PageRank
Dar, T.M.150.57
Raul G. Longoria2638.28