Eye-In-Hand Visual Servoing for Accurate Shooting in Pool Robotics

Lam, Joseph S.; Greenspan, Michael

doi:10.1109/crv.2008.30

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…The w H g is the current pose of end effector in the O w À X w Y w Z w . The grasping model equation (25) of 4-R(2-SS) parallel robot is also improved by motion error obtained from hand-eye calibration based on differential motion as follows:…”

Section: Improved Eye-to-hand Model and Model Solving Methodsmentioning

confidence: 99%

“…To achieve accurate and stable grasping of fruit, the end effector of parallel robot needs to move to the position of fruit and grasp the fruit with an optimal grasping pose in the fruit sorting system based on 4-R(2-SS) parallel robot with 4-DOF. Assuming that the optimal grasping pose is H p , to make the end-effector change from the current pose H g to the optimal grasping pose H p accurately, the H p needs to be transformed and represented as w H p in the O w À X w Y w Z w , as shown in equation (25).…”

Section: Hand-eye Calibration and Grasping Pose Calculation Based On Improved Eye-to-hand Model And Model Solving Methods For 4-r(2-ss) Pmentioning

confidence: 99%

“…Currently, the solving methods for hand–eye model mainly include homogeneous transformation method, 23,24 matrix rearrangement method, 25 method based on matrix vectorization and Kronecker product, 26 dual quaternion method, 27 and Lie group method. 28 The rotation matrix is expressed by rotating an angle around an axis in homogeneous transformation method, which is obtained by calculating the unit vector and rotation angle of rotation axis.…”

Section: Introductionmentioning

confidence: 99%

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Hand–eye calibration and grasping pose calculation with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot

Zhang

Gao

2020

International Journal of Advanced Robotic Systems

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Due to motion constraint of 4-R(2-SS) parallel robot, it is difficult to calculate the translation component of hand–eye calibration based on the existing model solving method accurately. Additionally, the camera calibration error, robot motion error, and invalid calibration motion poses make it difficult to achieve fast and accurate online hand–eye calibration. Therefore, we propose a hand–eye calibration method with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot by improving the existing eye-to-hand model and solving method. Firstly, the eye-to-hand model of single camera is improved and the robot motion error in the improved model is compensated to reduce the influence of camera calibration error and robot motion error on model accuracy. Secondly, the vertical-component of hand–eye calibration is corrected based on vertical constraint between calibration plate and end effector in parallel robot to calculate the pose and motion error in calibration of 4-R(2-SS) parallel robot accurately. Thirdly, the nontrivial solution constraint of eye-to-hand model is constructed and adopted to remove invalid calibration motion poses and plan calibration motion. Finally, the proposed method was verified by experiments with a fruit sorting system based on 4-R(2-SS) parallel robot. Compared with random motion, the existing model, and solving method, the average time of online calibration based on planned motion decreases by 29.773 s and the average error of calibration based on the improved model and solving method decreases by 151.293. The proposed method can improve the accuracy and efficiency of hand–eye calibration of 4-R(2-SS) parallel robot effectively and further realize accurate and fast grasping.

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Section: Improved Eye-to-hand Model and Model Solving Methodsmentioning

confidence: 99%

Section: Hand-eye Calibration and Grasping Pose Calculation Based On Improved Eye-to-hand Model And Model Solving Methods For 4-r(2-ss) Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hand–eye calibration and grasping pose calculation with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot

Zhang

Gao

2020

International Journal of Advanced Robotic Systems

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“…When the robot is servoed to its shot position, as determined by the GVS, it accumulates error. By analyzing the LVS image, and comparing the line connecting the current cue and object ball centers with the ideal line, it is possible to calculate transformations which can correct for the robot positioning error [14].…”

Section: A Lvs Correctionmentioning

confidence: 99%

“…We have developed two different methods to align the robot position with the LVS ideal line [14]. The simpler of the two, called the image-based method, is an iterative method based purely on 2D LVS image data.…”

Section: A Lvs Correctionmentioning

confidence: 99%

Toward a Competitive Pool Playing Robot: Is Computational Intelligence Needed to Play Robotic Pool?

Greenspan

Lam

Leckie

et al. 2007

2007 IEEE Symposium on Computational Intelligence and Games

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Abstract-This paper describes the development of DeepGreen, an intelligent robotic system that is currently in development to play competitive pool against a proficient human opponent. The design philosophy and the main system components are presented, and the progress to date is summarized. We also address a common misconception about the game of pool, i.e. that it is purely a game of physical skill, requiring little or no intelligence or strategy. We explain some of the difficulties in developing a vision-based system with a high degree of positional accuracy. We further demonstrate that even if perfect accuracy were possible, it is still beneficial and necessary to play strategically.

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An Iterative Algebraic Approach to TCF Matrix Estimation

Lam

Greenspan

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Eye-In-Hand Visual Servoing for Accurate Shooting in Pool Robotics

Cited by 3 publications

References 13 publications

Hand–eye calibration and grasping pose calculation with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot

Hand–eye calibration and grasping pose calculation with motion error compensation and vertical-component correction for 4-R(2-SS) parallel robot

Toward a Competitive Pool Playing Robot: Is Computational Intelligence Needed to Play Robotic Pool?

An Iterative Algebraic Approach to TCF Matrix Estimation

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