Predictive Control for Constrained Image-Based Visual Servoing

This paper deals with the image-based visual servoing (IBVS), subject to constraints. Robot workspace limitations, visibility constraints, and actuators limitations are addressed. These constraints are formulated into state, output, and input constraints, respectively. Based on the predictive-contro...

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Veröffentlicht in:IEEE transactions on robotics Jg. 26; H. 5; S. 933 - 939
Hauptverfasser: Allibert, G, Courtial, E, Chaumette, F
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.10.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Applied sciences
Artificial intelligence
Automatic
Cameras
Computer science; control theory; systems
Computer simulation
Control system synthesis
Control theory. Systems
Design optimization
Engineering Sciences
Exact sciences and technology
Materials handling
Mathematical model
Mathematical models
Matrix
Nonlinearity
Pattern recognition. Digital image processing. Computational geometry
Predictive control
Predictive models
Robotics
Robots
Trajectory
Visibility
Visual
Visual servoing
Visualization
Control synthesis
Visual servoing
Non linear programming
Output signal
Modeling
Robotics
Constrained optimization
Control constraint
Non linear model
Visibility
System with six degrees of freedom
Actuator
Predictive control
State constraint
Workspace
ISSN:1552-3098, 1941-0468
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Beschreibung
Zusammenfassung:This paper deals with the image-based visual servoing (IBVS), subject to constraints. Robot workspace limitations, visibility constraints, and actuators limitations are addressed. These constraints are formulated into state, output, and input constraints, respectively. Based on the predictive-control strategy, the IBVS task is written into a nonlinear optimization problem in the image plane, where the constraints can be easily and explicitly taken into account. Second, the contribution of the image prediction and influence of the prediction horizon are pointed out. The image prediction is obtained due to a model. The latter can be a local model based on the interaction matrix or a nonlinear global model based on 3-D data. Its choice is discussed with respect to the constraints to be handled. Finally, simulations that were obtained with a 6-degree-of-freedom (DOF) free-flying camera highlight the potential advantages of the proposed approach with respect to the image prediction and the constraint handling.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2010.2056590