Abstract
This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.
Highlights
In order to modify the behaviour of robots in dealing with unstructured environments, vision sensors are commonly used to provide contact-less information about the environment.[1]
To tackle the above-mentioned problems, we proposed an optimized VS method called hybrid decoupled visual servoing (HDVS)
In order to evaluate the effectiveness of the HDVS method, various scenarios have been studied and compared with hybrid visual servoing (HVS), image-based visual servoing (IBVS) and position-based visual servoing (PBVS) approaches
Summary
In order to modify the behaviour of robots in dealing with unstructured environments, vision sensors are commonly used to provide contact-less information about the environment.[1] Real-time information from the camera image provides feedback to control the motion of a robot. VS contributes to modify the system to compensate deficiencies of a mechanism and to relax the mechanical inaccuracy and stiffness of the robot.[2] This ability comes from the fact that the feature errors are regulated directly in the task space.[3] Despite this fact, how to use the image information to control the motion of a robot always been a major challenge in robotics. VS control approaches are broadly classified into three categories: image-based visual servoing (IBVS), position-based visual servoing (PBVS) and hybrid visual servoing (HVS)
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More From: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
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