Abstract
This paper presents a way to solve inverse kinematics of a 7-DOF manipulator using artificial neural networks. The manipulator consists of a 6-DOF articulated arm installed on a linear guide system to increase the workspace of the robot. The purpose of this paper is to provide an alternative to the traditional and complicated way to solve inverse kinematics by using artificial neural networks. The training data is generated from MATLAB after obtaining the DH parameters and workspace of the manipulator. Then, it was fed to the convolutional neural architecture to obtain a model for the manipulator. The input of the CNN is the end effector desired pose, and the outputs are the position angles of each joint. Two different architectures of artificial neural networks are compared to decide the most efficient architecture that produces the most accurate descriptive model of the manipulator.
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Acknowledgment
The authors would like to thank Eng. Bahaa Ashraf, Eng. Ahmed Tarek, Eng. Mahmoud Bakr, Eng. Lotfy Monir from Nile University for their design of the 7-DOF serial manipulator, and Eng. MennaAllah Soliman, teaching assistant in Mechatronics Engineering department at Nile University, for her great help and support in this work.
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Elkholy, H.A., Shahin, A.S., Shaarawy, A.W., Marzouk, H., Elsamanty, M. (2020). Solving Inverse Kinematics of a 7-DOF Manipulator Using Convolutional Neural Network. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_32
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