Arduino-based implementation of kinematics for a 4 DOF robot manipulator using artificial neural network

• Autorzy: Rashid Zaid Hikmat , Sarhan Riyadh Ahmed , Hassan Mohammed Salih

Identyfikatory

DOI

10.29354/diag/184235

• Języki publikacji: EN

Abstrakty

EN

Real-time motion control is basically dependent on robot kinematics analysis where there is no unique solution of the inverse kinematics of serial manipulators. The predictive artificial neural network is a powerful one for finding appropriate results based on training data. Therefore, an artificial neural network is proposed to implement on Arduino microcontroller for a 4-DOF robot manipulator where the inverse kinematics problem was partitioned to suit the low specification of the board CPU. With MATALB toolbox, multiple neural network configuration based were trained and experienced for the best fit of the dimensionless feedforward data that is obtained from the forward kinematics of reachable workspace with average MSE of 6.5e-7. The results showed the superior of the proposed networks reducing the joints error by 90 % at least with comparing to traditional one. An Arduino on-board program was developed to control the robot independly based on pre validated parameters of the network. The experimental results approved the proposed method to implement the robot in real time with maximum error of (± 0.15 mm) in end effector position. The presented approach can be applied to achieve a suitable solution of n-DOF self-depend robot implementation using micro stepping actuators.

Słowa kluczowe

EN

robot; kinematics; neural network; arduino; micro stepping

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 1
• Strony: art. no. 2024114
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Rashid Zaid Hikmat

• Al-Furat Al-Awsat Technical University, Technical Institute of Al-Mussaib, Iraq

• https://orcid.org/0000-0002-9800-4354

autor

Sarhan Riyadh Ahmed

• Al-Furat Al-Awsat Technical University, Technical Institute of Al-Mussaib, Iraq

• https://orcid.org/0000-0001-7095-684X

autor

Hassan Mohammed Salih

• Al-Furat Al-Awsat Technical University, Technical Institute of Al-Mussaib, Iraq

• https://orcid.org/0000-0002-5017-2404

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).