Abstract

industry. Investigating the movement of a manipulator with a substantial number of degrees of freedom (DOF) and finding an analytical resolution to the inverse kinematics is paramount in robot modeling. This study focuses on the kinematic modeling and analysis of a 6-DOF robotic manipulator. It aims to validate the accuracy of forward and inverse kinematics calculations using the Denavit-Hartenberg (D-H) parameterization method and MATLAB GUIDE, ensuring precise motion control and path planning for high-precision applications. The 6-DOF robotic manipulator was constructed using SolidWorks, featuring five revolute joints and one prismatic joint. The D-H parameters were established for the manipulator, and kinematic equations were derived. MATLAB GUIDE was employed to perform forward and inverse kinematics calculations, and the results were validated by comparing expected and obtained values. The forward kinematics results demonstrated minimal discrepancies between expected and obtained end-effector positions, with errors ranging from 0.01 to 0.02 units. Inverse kinematics calculations also showed minor deviations in joint angles, generally within 0.01 degrees, indicating a precise match between desired and computed values. These negligible errors confirm the reliability of the D-H parameter assignment and the kinematic equations used. This study successfully simplifies the complex calculations of forward and inverse kinematics for a six-DOF robotic manipulator, providing a robust foundation for precise motion control and path planning. The findings also validate the D-H parameterization method and highlight the practical importance of accurate kinematic modeling in high-precision applications.

Keyword

Robot manipulator, Denavit-Hartenberg (D-H), Forward kinematics, Inverse kinematics, Degree of freedom.

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Refference

[1]Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

[2]Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

[3]Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

[4]Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

[5]Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

[6]Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

[7]Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

[8]Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

[9]Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

[10]Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

[11]Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

[12]Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

[13]Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

[14]Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

[15]Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

[16]Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

[17]Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

[18]Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

[19]Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

[20]Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

[21]Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

[22]Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.