DocumentCode
495961
Title
Online trajectory following with position based force/vision control
Author
Alkkiomäki, Olli ; Kyrki, Ville ; Kälviäinen, Heikki ; Liu, Yong ; Handroos, Heikki
Author_Institution
Dept. of Inf. Technol., Lappeenranta Univ. of Technol., Lappeenranta, Finland
fYear
2009
fDate
22-26 June 2009
Firstpage
1
Lastpage
6
Abstract
Robot control in uncertain environments can greatly benefit from sensor based control. Visual sensing allows the robot to examine its surroundings and adapt to the environment. Force offers a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion several sensors measuring different modalities are combined together to give more accurate estimate of the environment. We present a method which fuses force and vision in an extended Kalman filter (EKF). A hybrid force controller is then set up to follow a trajectory based on the estimate from the EKF. The estimate allows a simple proportional force control to track a continuous trajectory reliably, where an unfiltered visual measurement becomes unstable. Experiments verify that the method can increase the stability of control considerably.
Keywords
Kalman filters; force control; image fusion; mobile robots; nonlinear filters; position control; robot vision; stability; uncertain systems; complementary sensory modality; extended Kalman filter; multimodal sensor fusion; object shape; online trajectory; position based force control; robot control; sensor based control; uncertain environment; unfiltered visual measurement; vision control; Force control; Force measurement; Force sensors; Fuses; Multimodal sensors; Robot control; Robot sensing systems; Sensor fusion; Shape measurement; Trajectory;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Robotics, 2009. ICAR 2009. International Conference on
Conference_Location
Munich
Print_ISBN
978-1-4244-4855-5
Electronic_ISBN
978-3-8396-0035-1
Type
conf
Filename
5174726
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