مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

Enhanced Joint Coordinate Space Estimation for Precise Position Control of SCARA Robot under Parametric Uncertainty

نویسندگان
سینا دیانت
مجید ساده دل
بهزاد سعیدی
دانشگاه تربیت مدرس-دانشکده مهندسی مکانیک
10.48311/mme.2025.24032
چکیده
In contemporary robotics, enhancing performance accuracy remains a critical and ongoing challenge. As robotic systems are increasingly utilized in precision-demanding applications such as surgical procedures and industrial welding, the demand for higher accuracy in end-effector positioning continues to grow. Numerous techniques have been proposed to address this issue. This study introduces a novel methodology that integrates the pseudo-inverse approach for error estimation—based on Taylor series expansion—with forward kinematics to extract uncertainty parameters. The refined inverse kinematics solutions, obtained through the Newton-Raphson method, are implemented on a SCARA robot for validation. The proposed approach yields substantial accuracy improvements, achieving a 99.8% enhancement in the x-direction and a 99.8% error reduction in the y-direction, thereby underscoring its potential for high-precision robotic applications.
کلیدواژه‌ها
Accurate Position Control
SCARA Robot
Forward and Inverse Kinematics
Pseudo-Inverse
Taylor Expansion
Newton-Raphson Method

موضوعات

عنوان مقاله English

Enhanced Joint Coordinate Space Estimation for Precise Position Control of SCARA Robot under Parametric Uncertainty

نویسندگان English

Sina Dianat
majid sadedel
Behzad Saeedi
Tarbiat Modares University
چکیده English

In contemporary robotics, enhancing performance accuracy remains a critical and ongoing challenge. As robotic systems are increasingly utilized in precision-demanding applications such as surgical procedures and industrial welding, the demand for higher accuracy in end-effector positioning continues to grow. Numerous techniques have been proposed to address this issue. This study introduces a novel methodology that integrates the pseudo-inverse approach for error estimation—based on Taylor series expansion—with forward kinematics to extract uncertainty parameters. The refined inverse kinematics solutions, obtained through the Newton-Raphson method, are implemented on a SCARA robot for validation. The proposed approach yields substantial accuracy improvements, achieving a 99.8% enhancement in the x-direction and a 99.8% error reduction in the y-direction, thereby underscoring its potential for high-precision robotic applications.

کلیدواژه‌ها English

Accurate Position Control
SCARA Robot
Forward and Inverse Kinematics
Pseudo-Inverse
Taylor Expansion
Newton-Raphson Method
[1] M. Dehghani, M. Mohammadi Moghadam, and P. Torabi, “Analysis, optimization and prototyping of a parallel RCM mechanism of a surgical robot for craniotomy surgery,” Industrial Robot: An International Journal, vol. 45, no. 1, pp. 78–88, Jan. 2018, doi: 10.1108/IR-08-2017-0144.
[2] M. Dehghani, R. A. McKenzie, R. A. Irani, and M. Ahmadi, “Robot-mounted sensing and local calibration for high-accuracy manufacturing,” Robot Comput Integr Manuf, vol. 79, p. 102429, Feb. 2023, doi: 10.1016/j.rcim.2022.102429.
[3] B. Saeedi, M. Mohammadi Moghaddam, and M. Sadedel, “Inverse kinematics analysis of a wrist rehabilitation robot using artificial neural network and adaptive Neuro-Fuzzy inference system,” Mechanics Based Design of Structures and Machines, pp. 1–49, May 2024, doi: 10.1080/15397734.2024.2356066.
[4] P.-N. Le and H.-J. Kang, “A New Manipulator Calibration Method for the Identification of Kinematic and Compliance Errors Using Optimal Pose Selection,” Applied Sciences, vol. 12, no. 11, p. 5422, May 2022, doi: 10.3390/app12115422.
[5] S. Asif and P. Webb, “Realtime Calibration of an Industrial Robot,” Applied System Innovation, vol. 5, no. 5, p. 96, Sep. 2022, doi: 10.3390/asi5050096.
[6] H. Yu, Q. Sun, C. Wang, and Y. Zhao, “Frequency response analysis of heavy-load palletizing robot considering elastic deformation,” Sci Prog, vol. 103, no. 1, p. 003685041989385, Jan. 2020, doi: 10.1177/0036850419893856.
[7] F. Cepolina and R. Razzoli, “Review of robotic surgery platforms and end effectors,” J Robot Surg, vol. 18, no. 1, p. 74, Feb. 2024, doi: 10.1007/s11701-023-01781-x.
[8] H.-N. Nguyen, T.-H. Nguyen, D.-T. Vo, and Q.-P. Pham, “MODEL BASED ROBOT CALIBRATION TECHNIQUE WITH CONSIDERATION OF JOINT COMPLIANCE,” JOURNAL OF TECHNOLOGY & INNOVATION, vol. 1, no. 1, pp. 06–09, Mar. 2021, doi: 10.26480/jtin.01.2021.06.09.
[9] Y. Wang, C. Zhao, D. Mei, G. Tang, L. Zhang, and D. Zhu, “Structural Design and Position Tracking of the Reconfigurable SCARA Robot by the Pre-Filter AFE PID Controller,” Applied Sciences, vol. 12, no. 3, p. 1626, Feb. 2022, doi: 10.3390/app12031626.
[10] H.-N. Nguyen, P.-N. Le, and H.-J. Kang, “A performance comparison of the full pose- and partial pose-based robot calibration for various types of robot manipulators,” Advances in Mechanical Engineering, vol. 13, no. 9, p. 168781402110477, Sep. 2021, doi: 10.1177/16878140211047754.
[11] T. Shu, S. Gharaaty, W. Xie, A. Joubair, and I. A. Bonev, “Dynamic Path Tracking of Industrial Robots With High Accuracy Using Photogrammetry Sensor,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 3, pp. 1159–1170, Jun. 2018, doi: 10.1109/TMECH.2018.2821600.
[12] Y. Zeng, W. Tian, D. Li, X. He, and W. Liao, “An error-similarity-based robot positional accuracy improvement method for a robotic drilling and riveting system,” The International Journal of Advanced Manufacturing Technology, vol. 88, no. 9–12, pp. 2745–2755, Feb. 2017, doi: 10.1007/s00170-016-8975-8.
[13] X. SHI, “Position and Attitude Measurement and Online Errors Compensation for KUKA Industrial Robots,” Journal of Mechanical Engineering, vol. 53, no. 8, p. 1, 2017, doi: 10.3901/JME.2017.08.001.
[14] H. X. Nguyen, H. Q. Cao, T. T. Nguyen, T. N.-C. Tran, H. N. Tran, and J. W. Jeon, “Improving Robot Precision Positioning Using a Neural Network Based on Levenberg Marquardt–APSO Algorithm,” IEEE Access, vol. 9, pp. 75415–75425, 2021, doi: 10.1109/ACCESS.2021.3082534.
[15] X. Li, H. Hu, and W. Ding, “Two Error Models for Calibrating SCARA Robots based on the MDH Model,” MATEC Web of Conferences, vol. 95, p. 08008, Feb. 2017, doi: 10.1051/matecconf/20179508008.
[16] Z. Wang, Y. Xu, Q. He, Z. Fang, G. Xu, and J. Fu, “Grasping pose estimation for SCARA robot based on deep learning of point cloud,” The International Journal of Advanced Manufacturing Technology, vol. 108, no. 4, pp. 1217–1231, May 2020, doi: 10.1007/s00170-020-05257-2.
[17] M. Švaco, B. Šekoranja, F. Šuligoj, and B. Jerbić, “Calibration of an Industrial Robot Using a Stereo Vision System,” Procedia Eng, vol. 69, pp. 459–463, 2014, doi: 10.1016/j.proeng.2014.03.012.
[18] S. Hayati, “Robot arm geometric link parameter estimation,” in The 22nd IEEE Conference on Decision and Control, IEEE, 1983, pp. 1477–1483. doi: 10.1109/CDC.1983.269783.
[19] A. Nubiola and I. A. Bonev, “Absolute calibration of an ABB IRB 1600 robot using a laser tracker,” Robot Comput Integr Manuf, vol. 29, no. 1, pp. 236–245, Feb. 2013, doi: 10.1016/j.rcim.2012.06.004.
[20] A. Nubiola and I. A. Bonev, “Absolute robot calibration with a single telescoping ballbar,” Precis Eng, vol. 38, no. 3, pp. 472–480, Jul. 2014, doi: 10.1016/j.precisioneng.2014.01.001.
[21] W. Zhenhua, X. Hui, C. Guodong, S. Rongchuan, and L. Sun, “A distance error based industrial robot kinematic calibration method,” Industrial Robot: An International Journal, vol. 41, no. 5, pp. 439–446, Aug. 2014, doi: 10.1108/IR-04-2014-0319.
[22] J. Zhou, H.-N. Nguyen, and H.-J. Kang, “Selecting Optimal Measurement Poses for Kinematic Calibration of Industrial Robots,” Advances in Mechanical Engineering, vol. 6, p. 291389, Jan. 2014, doi: 10.1155/2014/291389.
[23] W. Xiang and S. Yan, “Dynamic analysis of space robot manipulator considering clearance joint and parameter uncertainty: Modeling, analysis and quantification,” Acta Astronaut, vol. 169, pp. 158–169, Apr. 2020, doi: 10.1016/j.actaastro.2020.01.011.
[24] T. Tang, H. Luo, Y. Song, H. Fang, and J. Zhang, “Chebyshev inclusion function based interval kinetostatic modeling and parameter sensitivity analysis for Exechon-like parallel kinematic machines with parameter uncertainties,” Mech Mach Theory, vol. 157, p. 104209, Mar. 2021, doi: 10.1016/j.mechmachtheory.2020.104209.
[25] Q. Meng, X. Lai, Z. Yan, C. Y. Su, and M. Wu, “Motion Planning and Adaptive Neural Tracking Control of an Uncertain Two-Link Rigid-Flexible Manipulator With Vibration Amplitude Constraint,” IEEE Trans Neural Netw Learn Syst, vol. 33, no. 8, pp. 3814–3828, Aug. 2021, doi: 10.1109/TNNLS.2021.3054611.
[26] R. L. Truby, C. Della Santina, and D. Rus, “Distributed proprioception of 3d configuration in soft, sensorized robots via deep learning,” IEEE Robot Autom Lett, vol. 5, no. 2, pp. 3299–3306, Apr. 2020, doi: 10.1109/LRA.2020.2976320.
[27] J. Li, J. Wang, H. Peng, L. Zhang, Y. Hu, and H. Su, “Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction,” Neurocomputing, vol. 410, pp. 342–353, Oct. 2020, doi: 10.1016/j.neucom.2020.05.091.
[28] E. Z. Goh and T. Ali, “Robotic surgery: an evolution in practice,” Journal of Surgical Protocols and Research Methodologies, vol. 2022, no. 1, Jan. 2022, doi: 10.1093/jsprm/snac003.
[29] J. Klodmann et al., “An Introduction to Robotically Assisted Surgical Systems: Current Developments and Focus Areas of Research,” Current Robotics Reports, vol. 2, no. 3, pp. 321–332, Sep. 2021, doi: 10.1007/s43154-021-00064-3.
[30] C. Zhang and Z. Zhang, “Research on Joint Space Trajectory Planning of SCARA Robot Based on SimMechanics,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), IEEE, Mar. 2019, pp. 1446–1450. doi: 10.1109/ITNEC.2019.8729547.
[31] Y. Tian et al., “RoboKeyGen: Robot Pose and Joint Angles Estimation via Diffusion-based 3D Keypoint Generation,” Mar. 2024, [Online]. Available: http://arxiv.org/abs/2403.18259
[32] J. Li, L.-D. Yu, J.-Q. Sun, and H.-J. Xia, “A Kinematic Model for Parallel-Joint Coordinate Measuring Machine,” J Mech Robot, vol. 5, no. 4, Nov. 2013, doi: 10.1115/1.4025121.
[33] Q. Zhao, J. Guo, and J. Hong, “System Kinematic Reliability Analysis for Robotic Manipulators Under Rectangular and Spherical Tolerant Boundaries,” J Mech Robot, vol. 13, no. 1, Feb. 2021, doi: 10.1115/1.4047986.
[34] D. Zhang, S. Liu, J. Wu, Y. Wu, and J. Liu, “An active learning hybrid reliability method for positioning accuracy of industrial robots,” Journal of Mechanical Science and Technology, vol. 34, no. 8, pp. 3363–3372, Aug. 2020, doi: 10.1007/s12206-020-0729-8.
[35] J. Jang, D. Kim, and I. Kim, “Singularity Handling for Unbalanced Three-Phase Transformers in Newton-Raphson Power Flow Analyses Using Moore-Penrose Pseudo-Inverse,” IEEE Access, vol. 11, pp. 40657–40674, 2023, doi: 10.1109/ACCESS.2023.3269503.
مهندسی مکانیک مدرس
دوره 25، شماره 5
اردیبهشت 1404
صفحه 285-293