This paper presents a manipulator visualservoing algorithm with the Kalman Filter. The objective is to control the relative position and orientation between a robot's gripper and an object using a single camera m...
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ISBN:
(纸本)9781424435890
This paper presents a manipulator visualservoing algorithm with the Kalman Filter. The objective is to control the relative position and orientation between a robot's gripper and an object using a single camera mounted on the manipulator. The feature-basedvisualservoing control algorithm is used, which continually updates the pose of the robot relative to the object by using the differential changes in image plane. But the acquired image from a camera often has several noise elements such as image signal spatial quantization error, lens distortion, pixel error and etc... Its order to reduce the measurement errors, a Kalman Filter was adopted to estimate the motion states of a moving object. Its real implementation was performed for its effectiveness of the presented algorithm.
visualservoing is very useful for navigating robots to specific positions and orientations accurately and reliably. Unfortunately, the existing methods require accurate camera calibration tasks that are troublesome. ...
详细信息
ISBN:
(纸本)9788995003848
visualservoing is very useful for navigating robots to specific positions and orientations accurately and reliably. Unfortunately, the existing methods require accurate camera calibration tasks that are troublesome. Whenever a camera is changed or intrinsic parameters become different, we should carry out camera calibration tasks. Methods in order to cope with this problem is so called self-calibration techniques. Until now, self-calibration techniques just have considered the case of static self-calibration where these estimated intrinsic parameters are not used to control the robot. Only a few researchers have recently developed the case of dynamic self-calibration where estimated intrinsic parameters are used to control the robot. In this paper, a visualservoing procedure which is performed in parallel to a self-calibration algorithm based on simplified kruppa equations is proposed. Simulation results show that estimation of both constant and varying intrinsic parameters is efficient and the the robot converge to the desired positions.
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