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Z. Bingul - One of the best experts on this subject based on the ideXlab platform.
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Inverse Kinematics solutions for industrial robot manipulators with offset wrists
Applied Mathematical Modelling, 2014Co-Authors: Serdar Kucuk, Z. BingulAbstract:Abstract In this paper, the Inverse Kinematics solutions for 16 industrial 6- D egrees-of- F reedom (DOF) robot manipulators with offset wrists are solved analytically and numerically based on the existence of the closed form equations. A new numerical algorithm is proposed for the Inverse Kinematics of the robot manipulators that cannot be solved in closed form. In order to illustrate the performance of the N ew I nverse K inematics A lgorithm (NIKA), the simulation results attained from NIKA are compared with those obtained from well-known N ewton– R aphson A lgorithm (NRA). The Inverse Kinematics solutions of two robot manipulators with offset wrists are given as examples. In order to have a complete idea, the Inverse Kinematics solution techniques for 16 industrial robot manipulators are also summarized in a table.
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The Inverse Kinematics solutions of fundamental robot manipulators with offset wrist
IEEE International Conference on Mechatronics 2005. ICM '05., 2005Co-Authors: S. Kucuck, Z. BingulAbstract:In this work, the Inverse Kinematics of sixteen fundamental 6-DOF robot manipulators with offset wrist were solved analytically and numerically. Analytical and numerical techniques are the most common approaches for solving Inverse Kinematics problems. They are generally desired to be solved analytically in order to have complete solution and fast computation. This approach is also called as closed form solution and in the absence of it, numerical techniques are used for solving the Inverse Kinematics problem. As examples, the Inverse Kinematics solutions of RS (Scara robot), CS (cylindrical robot), and NN (spherical robot) robot manipulators with offset wrist were presented in this paper. Also, the Inverse Kinematics solution techniques for sixteen fundamental robot manipulators equipped with offset wrist were summarized in a table.
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the Inverse Kinematics solutions of industrial robot manipulators
International Conference on Mechatronics, 2004Co-Authors: Serdar Kucuk, Z. BingulAbstract:The Inverse Kinematics problem of robot manipulators is solved analytically in order to have complete and simple solutions to the problem. This approach is also called as a closed form solution of robot Inverse Kinematics problem. In this paper, the Inverse Kinematics of sixteen industrial robot manipulators classified by Huang and Milenkovic were solved in closed form. Each robot manipulator has an Euler wrist whose three axes intersect at a common point. Basically, five trigonometric equations were used to solve the Inverse Kinematics problems. Robot manipulators can be mainly divided into four different group based on the joint structure. In this work, the Inverse Kinematics solutions of SN (cylindrical robot with dome), CS (cylindrical robot), NR (articulated robot) and CC (selectively compliant assembly robot arm-SCARA, Type 2) robot manipulator belonging to each group mentioned above are given as an example. The number of the Inverse Kinematics solutions for the other robot manipulator was also summarized in a table.
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The Inverse Kinematics solutions of industrial robot manipulators
Proceedings of the IEEE International Conference on Mechatronics 2004 ICM'04, 2004Co-Authors: Serdar Küc̈ük, Z. BingulAbstract:The Inverse Kinematics problem of robot manipulators is desired to be solved analytically in order to have complete and simple solutions to the problem. This approach is also called as a closed form solution of robot Inverse Kinematics problem. In this paper, the Inverse Kinematics of sixteen industrial robot manipulators classified by Huang and Milenkovic [1] were solved in closed form. Each robot manipulator has an Euler wrist (Figure 1) whose three axes intersect at a common point. Basically, five trigonometric equations were used to solve the Inverse Kinematics problems. Robot manipulators can be mainly divided into four different group based on the joint structure. In this work, the Inverse Kinematics solutions of SN (cylindrical robot with dome), CS (cylindrical robot), NR (articulated robot) and CC (selectively compliant assembly robot arm-SCARA, Type 2) robot manipulator belonging to each group mentioned above are given as an example. The number of the Inverse Kinematics solutions for the other robot manipulator was also summarized in a table.
S. Tachi - One of the best experts on this subject based on the ideXlab platform.
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A modular neural network architecture for Inverse Kinematics model learning
Neurocomputing, 2001Co-Authors: E. Oyama, Arvin Agah, Karl F. Macdorman, Taro Maeda, S. TachiAbstract:In order to reach an object, we need to solve the Inverse Kinematics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector of the arm. The learning of the Inverse Kinematics model for calculating every joint angle that would result in a speci"c hand position is important. However, the Inverse Kinematics function of the human arm is a multi-valued and discontinuous function. It is di$cult for a well-known continuous neural network to approximate such a function. In order to overcome the discontinuity of the Inverse Kinematics function, a novel modular neural network architecture is proposed in this paper. 2001 Published by Elsevier Science B.V.
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ICRA - Modular neural net system for Inverse Kinematics learning
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000Co-Authors: E. Oyama, S. TachiAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers, However, conventional learning methodologies do not pay enough attention the the discontinuity of the Inverse Kinematics system of typical robot arms with joint limits. The Inverse Kinematics system of the robot arms is a multi-valued and discontinuous function. Since it is difficult for a well-known multi-layer neural network to approximate such a function, a correct Inverse Kinematics model for the end-effector's overall position and orientation cannot be obtained by using a single neural network. In order to overcome the discontinuity of the Inverse Kinematics function, we propose a modular neural network system for the Inverse Kinematics model learning. We also propose the online learning and control method for trajectory tracking.
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Inverse Kinematics learning by modular architecture neural networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999Co-Authors: E. Oyama, S. TachiAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the Inverse Kinematics system of typical robot arms with joint limits. The Inverse Kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct Inverse Kinematics model for the end-effector's overall position and orientation cannot be obtained by the conventional methods. In order to overcome the drawbacks of the Inverse Kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the Inverse Kinematics model learning.
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IJCNN - Inverse Kinematics learning by modular architecture neural networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999Co-Authors: E. Oyama, S. TachiAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the Inverse Kinematics system of typical robot arms with joint limits. The Inverse Kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct Inverse Kinematics model for the end-effector's overall position and orientation cannot be obtained by the conventional methods. In order to overcome the drawbacks of the Inverse Kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the Inverse Kinematics model learning.
E. Oyama - One of the best experts on this subject based on the ideXlab platform.
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A modular neural network architecture for Inverse Kinematics model learning
Neurocomputing, 2001Co-Authors: E. Oyama, Arvin Agah, Karl F. Macdorman, Taro Maeda, S. TachiAbstract:In order to reach an object, we need to solve the Inverse Kinematics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector of the arm. The learning of the Inverse Kinematics model for calculating every joint angle that would result in a speci"c hand position is important. However, the Inverse Kinematics function of the human arm is a multi-valued and discontinuous function. It is di$cult for a well-known continuous neural network to approximate such a function. In order to overcome the discontinuity of the Inverse Kinematics function, a novel modular neural network architecture is proposed in this paper. 2001 Published by Elsevier Science B.V.
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ICRA - Inverse Kinematics learning by modular architecture neural networks with performance prediction networks
Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001Co-Authors: E. Oyama, Arvin Agah, Nak Young Chong, Taro MaedaAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers. However, the Inverse Kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct Inverse Kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the Inverse Kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the Inverse Kinematics function. The proposed system uses the forward Kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the Inverse Kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.
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ICRA - Modular neural net system for Inverse Kinematics learning
Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), 2000Co-Authors: E. Oyama, S. TachiAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers, However, conventional learning methodologies do not pay enough attention the the discontinuity of the Inverse Kinematics system of typical robot arms with joint limits. The Inverse Kinematics system of the robot arms is a multi-valued and discontinuous function. Since it is difficult for a well-known multi-layer neural network to approximate such a function, a correct Inverse Kinematics model for the end-effector's overall position and orientation cannot be obtained by using a single neural network. In order to overcome the discontinuity of the Inverse Kinematics function, we propose a modular neural network system for the Inverse Kinematics model learning. We also propose the online learning and control method for trajectory tracking.
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Inverse Kinematics learning by modular architecture neural networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999Co-Authors: E. Oyama, S. TachiAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the Inverse Kinematics system of typical robot arms with joint limits. The Inverse Kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct Inverse Kinematics model for the end-effector's overall position and orientation cannot be obtained by the conventional methods. In order to overcome the drawbacks of the Inverse Kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the Inverse Kinematics model learning.
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IJCNN - Inverse Kinematics learning by modular architecture neural networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999Co-Authors: E. Oyama, S. TachiAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers. However, conventional learning methodologies do not pay enough attention to the discontinuity of the Inverse Kinematics system of typical robot arms with joint limits. The Inverse Kinematics system of the robot arms, including a human arm with a wrist joint, is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct Inverse Kinematics model for the end-effector's overall position and orientation cannot be obtained by the conventional methods. In order to overcome the drawbacks of the Inverse Kinematics solver consisting of a single neural network, we propose a novel modular neural network architecture for the Inverse Kinematics model learning.
Serdar Kucuk - One of the best experts on this subject based on the ideXlab platform.
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Inverse Kinematics solutions for industrial robot manipulators with offset wrists
Applied Mathematical Modelling, 2014Co-Authors: Serdar Kucuk, Z. BingulAbstract:Abstract In this paper, the Inverse Kinematics solutions for 16 industrial 6- D egrees-of- F reedom (DOF) robot manipulators with offset wrists are solved analytically and numerically based on the existence of the closed form equations. A new numerical algorithm is proposed for the Inverse Kinematics of the robot manipulators that cannot be solved in closed form. In order to illustrate the performance of the N ew I nverse K inematics A lgorithm (NIKA), the simulation results attained from NIKA are compared with those obtained from well-known N ewton– R aphson A lgorithm (NRA). The Inverse Kinematics solutions of two robot manipulators with offset wrists are given as examples. In order to have a complete idea, the Inverse Kinematics solution techniques for 16 industrial robot manipulators are also summarized in a table.
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the Inverse Kinematics solutions of industrial robot manipulators
International Conference on Mechatronics, 2004Co-Authors: Serdar Kucuk, Z. BingulAbstract:The Inverse Kinematics problem of robot manipulators is solved analytically in order to have complete and simple solutions to the problem. This approach is also called as a closed form solution of robot Inverse Kinematics problem. In this paper, the Inverse Kinematics of sixteen industrial robot manipulators classified by Huang and Milenkovic were solved in closed form. Each robot manipulator has an Euler wrist whose three axes intersect at a common point. Basically, five trigonometric equations were used to solve the Inverse Kinematics problems. Robot manipulators can be mainly divided into four different group based on the joint structure. In this work, the Inverse Kinematics solutions of SN (cylindrical robot with dome), CS (cylindrical robot), NR (articulated robot) and CC (selectively compliant assembly robot arm-SCARA, Type 2) robot manipulator belonging to each group mentioned above are given as an example. The number of the Inverse Kinematics solutions for the other robot manipulator was also summarized in a table.
Taro Maeda - One of the best experts on this subject based on the ideXlab platform.
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A modular neural network architecture for Inverse Kinematics model learning
Neurocomputing, 2001Co-Authors: E. Oyama, Arvin Agah, Karl F. Macdorman, Taro Maeda, S. TachiAbstract:In order to reach an object, we need to solve the Inverse Kinematics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector of the arm. The learning of the Inverse Kinematics model for calculating every joint angle that would result in a speci"c hand position is important. However, the Inverse Kinematics function of the human arm is a multi-valued and discontinuous function. It is di$cult for a well-known continuous neural network to approximate such a function. In order to overcome the discontinuity of the Inverse Kinematics function, a novel modular neural network architecture is proposed in this paper. 2001 Published by Elsevier Science B.V.
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ICRA - Inverse Kinematics learning by modular architecture neural networks with performance prediction networks
Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001Co-Authors: E. Oyama, Arvin Agah, Nak Young Chong, Taro MaedaAbstract:Inverse Kinematics computation using an artificial neural network that learns the Inverse Kinematics of a robot arm has been employed by many researchers. However, the Inverse Kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct Inverse Kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the Inverse Kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the Inverse Kinematics function. The proposed system uses the forward Kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the Inverse Kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.
