DocumentCode :
2416752
Title :
The application of particle filtering to grasping acquisition with visual occlusion and tactile sensing
Author :
Zhang, Li Emma ; Trinkle, Jeffrey C.
Author_Institution :
Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY, USA
fYear :
2012
fDate :
14-18 May 2012
Firstpage :
3805
Lastpage :
3812
Abstract :
Advanced grasp control algorithms could benefit greatly from accurate tracking of the object as well as an accurate all-around knowledge of the system when the robot attempts a grasp. This motivates our study of the G-SL(AM)2 problem, in which two goals are simultaneously pursued: object tracking relative to the hand and estimation of parameters of the dynamic model. We view the G-SL(AM)2 problem as a filtering problem. Because of stick-slip friction and collisions between the object and hand, suitable dynamic models exhibit strong nonlinearities and jump discontinuities. This fact makes Kalman filters (which assume linearity) and extended Kalman filters (which assume differentiability) inapplicable, and leads us to develop a particle filter. An important practical problem that arises during grasping is occlusion of the view of the object by the robot´s hand. To combat the resulting loss of visual tracking fidelity, we designed a particle filter that incorporates tactile sensor data. The filter is evaluated off-line with data gathered in advance from grasp acquisition experiments conducted with a planar test rig. The results show that our particle filter performs quite well, especially during periods of visual occlusion, in which it is much better than the same filter without tactile data.
Keywords :
Kalman filters; control nonlinearities; manipulators; object tracking; particle filtering (numerical methods); robot vision; tactile sensors; G-SL(AM)2 problem; Kalman filters; accurate object tracking; dynamic models; grasp control algorithms; grasping acquisition; jump discontinuities; particle filtering; planar test rig; stick-slip friction; strong nonlinearities; tactile sensing; visual occlusion; visual tracking fidelity; Equations; Friction; Grasping; Mathematical model; Robot sensing systems; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2012 IEEE International Conference on
Conference_Location :
Saint Paul, MN
ISSN :
1050-4729
Print_ISBN :
978-1-4673-1403-9
Electronic_ISBN :
1050-4729
Type :
conf
DOI :
10.1109/ICRA.2012.6225125
Filename :
6225125
Link To Document :
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