J-021

Random Contact Strategy Using the Kalman Filter to Solve the Robotic Contact Uncertainty Problem

Authors: Alvin Chua (1), Jayantha Katupitiya (2), Joris De Schutter (3)
Affiliation: (1) Dept. of Mechanical Engineering, De La Salle University, The Philippines
(2) School of Mechanical and Manufacturing Engineering, The University of New Southe Wales, Australia
(3) Dept. of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium

Abstract
This paper addresses the problem of finding the uncertainties present in a robotic contact. There are two kinds of uncertainties: grasping uncertainties and contact uncertainties.  Grasping uncertainty vector contains errors (angles and displacements) associated with improper grasping.  Contact uncertainty vector contains errors in angles and positions of nominal contact.  A force sensor is used together with Kalman Filters to solve for the uncertainties. The straightforward use of Kalman Filters is found to be effective in finding only some of the uncertainties. The quantities that form dependencies cannot be estimated in this manner. This dependency brings about the problem of observability. The unobservable quantities can be determined using a sequence of contacts. The error covariance matrix of the Kalman Filter can indicate the directions of dependency and accuracy of the values estimated. A new contact in any of the dependent directions can be randomly chosen as the next contact to try.  The relational transformations between contacts are used to eventually obtain the complete solution. A two dimensional contact situation will be used to demonstrate the effectiveness of the method. Experimental data are also presented to prove the validity of the procedure. Due to the non-linear relationship between the uncertainties and the forces, an Extended Kalman Filter (EKF) has been used.

Alvin Chua
Assistant Professor
Dept. of Mechanical Engineering
De La Salle University
2401 Taft Avenue
1004 Manila, The Philippines

Jayantha Katupitiya
Lecturer
School of Mechanical and Manufacturing Engineering
The University of New Southe Wales
Sydney NSW 2052, Australia
J.Katupitiya@unsw.edu.au

Joris De Schutter
Professor
Dept. of Mechanical Engineering
Katholieke Universiteit Leuven
Celestijnenlaan 300B
B-3001 Heverlee, Belgium