The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Sang-rok Oh - One of the best experts on this subject based on the ideXlab platform.
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IROS - An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
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An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
Joonhee Jo - One of the best experts on this subject based on the ideXlab platform.
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IROS - An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
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An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
Yonghwan Oh - One of the best experts on this subject based on the ideXlab platform.
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IROS - An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
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An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
Duc Trong Tran - One of the best experts on this subject based on the ideXlab platform.
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IROS - An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
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An on-line Gravity estimation method using inverse Gravity regressor for robot manipulator control
2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2015Co-Authors: Joonhee Jo, Duc Trong Tran, Yonghwan Oh, Sang-rok OhAbstract:When a robotic manipulator is controlled, computing Gravity Force of the robot is the primary issue. Exact model parameters are not easy to be known in the practical robot system due to the uncertainty of the robot dynamics. Hence, the Gravity Force is presented by a combination of Gravity regressor and robot dynamic parameters and is compensated by the estimation of uncertain robot dynamic parameters. Previous researches conducted estimation by using transpose of Gravity regressor and full form of dynamic parameters which is not general form however this paper estimates the Gravity Force using the generalized Gravity regressor which is regardless of the dimension and structure of the robot under the quasi-static state. Once the estimation is completed, the estimated value can be used to compute the gravitational Force and control the robot. It is shown that the generalized decomposition of Gravity regressor and estimation process. The results are validated through an experiment by implementing the algorithm on an upper-body dual arm robot.
Nghe Huan Quach - One of the best experts on this subject based on the ideXlab platform.
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Estimation and Compensation of Gravity and Friction Forces for Robot Arms: Theory and Experiments
Journal of Intelligent and Robotic Systems, 2001Co-Authors: Nghe Huan QuachAbstract:This paper considers the estimation and compensation of the unknown Gravity Force and static friction for robot motion control. Utilizing the stability feature of PD set-point control, the estimates of Gravity-related parameters and static friction can be solved from two steady state equations obtained by stopping robots at two nonsingular positions. The estimates obtained can then be used to eliminate the position error. Under a mild assumption that the mass center of each robot link is distributed on a straight line connecting two adjacent joints, the Gravity Force regression matrix becomes upper-triangle which can significantly simplify the algorithm. The positive experimental result obtained for practical verification is also presented.
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A 3-step set-point control algorithm for robot arms
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000Co-Authors: Nghe Huan QuachAbstract:For robot arm set-point control, a novel PD based algorithm for the estimation and compensation of Gravity Force and static friction is proposed. Based on the linear-in-parameter property of the Gravity Force and the steady state feature of PD set-point control, the algorithm modifies the set-points according to the steady state position errors. Using the steady state equations near the given set-points the unknown static friction Force and Gravity Force can be calculated online. This information can then be used to compensate the effects of Gravity Force and static friction to eliminate set-point errors.
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ICRA - A 3-step set-point control algorithm for robot arms
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000Co-Authors: Nghe Huan QuachAbstract:For robot arm set-point control, a novel PD based algorithm for the estimation and compensation of Gravity Force and static friction is proposed. Based on the linear-in-parameter property of the Gravity Force and the steady state feature of PD set-point control, the algorithm modifies the set-points according to the steady state position errors. Using the steady state equations near the given set-points the unknown static friction Force and Gravity Force can be calculated online. This information can then be used to compensate the effects of Gravity Force and static friction to eliminate set-point errors.
