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초록
영어
In this paper, we present a novel stable hybrid vision-based robot control algorithm. This method utilizes probabilistic integration of image-based and position-based visual servo con- trollers to produce an improved vision-based robot control. A probabilistic framework is employed to derive the integration scheme. Appropriate probabilistic importance functions are dened for the basic two algorithms to characterize their suitability in the task. The integrated algorithm has superior performance both in image and Cartesian spaces. Exper- iments validate this claim.
목차
Abstract
1 Introduction
2 Background of Visual Servoing
2.1 Image-based visual servoing
2.2 Position-based visual servoing
3 The Integration Framework
3.1 Probabilistic integration framework
3.2 The Integration Rule of IBVS and PBVS Controllers
3.3 Computation of the Importance weights for classical visual servoing
3.4 Stability Analysis of the Hybrid visual control law
4 Experimental Evaluation
4.1 The Experimental Setup
4.2 Results from Positioning Tasks
5 Conclusions
References
1 Introduction
2 Background of Visual Servoing
2.1 Image-based visual servoing
2.2 Position-based visual servoing
3 The Integration Framework
3.1 Probabilistic integration framework
3.2 The Integration Rule of IBVS and PBVS Controllers
3.3 Computation of the Importance weights for classical visual servoing
3.4 Stability Analysis of the Hybrid visual control law
4 Experimental Evaluation
4.1 The Experimental Setup
4.2 Results from Positioning Tasks
5 Conclusions
References
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