Integrated Robotic Arm Control: Inverse Kinematics, Trajectory Planning, and Performance Evaluation for Automated Welding
DOI:
https://doi.org/10.51278/ajse.v2i2.1021Keywords:
Robotic Arm Manipulators, Inverse Kinematics, Trajectory Error AnalysisAbstract
This research delves into the automated functionality of robotic arm manipulators, a hallmark of Industry 4.0, within the manufacturing sector. The study focuses on precise movement adhering to predetermined trajectories, addressing the vital aspects of inverse kinematics and trajectory planning in robotic arm control. Utilizing the Matlab robotic toolbox, the research conducts simulations of inverse kinematic and trajectory planning. An experimental setup involving a robotic arm controlled by an Arduino Mega 2560 microcontroller, embedded with the inverse kinematic algorithm and trajectory planning, is executed. Data acquisition involves inputting coordinates and orientation for automatic welding along a straight path. Joint angles are measured using rotary encoders and converted into Cartesian coordinates to determine the end-effector's position. Discrepancy analysis compares measured joint angles with simulation values, revealing error margins. Movement quality of the robotic arm is assessed through Capability Processes (CP) evaluation. Results indicate disparities between experimental and simulated values. At input coordinates (400mm, 0mm, 300mm), joint angle errors of 2.51º, 0.98º, and 1.48º are observed for joints 2, 3, and 5, respectively. Similarly, at input coordinates (300mm, 0mm, 300mm), joint angle errors of 1.17º, 1.5º, and 2.7º are registered for the same joints. Trajectory error analysis during straight welding reveals average errors of 2.25 mm and 10.57 mm along the x and y axes. Mean absolute errors for joints 2, 3, and 5 are 1.9º, 0.48º, and 1.91º.
Keywords: Robotic Arm Manipulators, Inverse Kinematics, Trajectory Error Analysis
References
[2] J. D. P. Aguilar and J. V. Padilla, “Control de un brazo robótico usando el hardware kinect® de microsoft,” Prospectiva, vol. 11, no. 2, 2014, doi: 10.15665/rp.v11i2.43.
[3] S. Broota, “Building of Inmoov Robotic Arm for Performing Various Operations,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 1, 2022, doi: 10.22214/ijraset.2022.39804.
[4] J. Cai, J. Deng, W. Zhang, and W. Zhao, “Modeling Method of Autonomous Robot Manipulator Based on D-H Algorithm,” Mob. Inf. Syst., vol. 2021, 2021, doi: 10.1155/2021/4448648.
[5] C. Canali et al., “Design of a Novel Long-Reach Cable-Driven Hyper-Redundant Snake-Like Manipulator for Inspection and Maintenance,” Appl. Sci., vol. 12, no. 7, 2022, doi: 10.3390/app12073348.
[6] L. Cheng, D. Li, G. Yu, Z. Zhang, and S. Yu, “Robotic arm control system based on brain-muscle mixed signals,” Biomed. Signal Process. Control, vol. 77, 2022, doi: 10.1016/j.bspc.2022.103754.
[7] G. Chowdhary, M. Gazzola, G. Krishnan, C. Soman, and S. Lovell, “Soft robotics as an enabling technology for agroforestry practice and research,” Sustain., vol. 11, no. 23, 2019, doi: 10.3390/su11236751.
[8] R. Dong, J. Du, Y. Liu, A. A. Heidari, and H. Chen, “An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms,” Front. Neuroinform., vol. 17, 2023, doi: 10.3389/fninf.2023.1096053.
[9] Y. Gao et al., “A Dual-Armed Robotic Puncture System: Design, Implementation and Preliminary Tests,” Electron., vol. 11, no. 5, 2022, doi: 10.3390/electronics11050740.
[10] A. Ibarguren, I. Eimontaite, J. L. Outón, and S. Fletcher, “Dual arm co-manipulation architecture with enhanced human–robot communication for large part manipulation,” Sensors (Switzerland), vol. 20, no. 21, 2020, doi: 10.3390/s20216151.
[11] A. Kawamura, B. Gang, M. Uemura, and S. Kawamura, “Mechanism and control of robotic arm using rotational counterweights,” in Proceedings - IEEE International Conference on Robotics and Automation, 2015. doi: 10.1109/ICRA.2015.7139567.
[12] H. J. Kim, A. Kawamura, Y. Nishioka, and S. Kawamura, “Mechanical design and control of inflatable robotic arms for high positioning accuracy,” Adv. Robot., vol. 32, no. 2, 2018, doi: 10.1080/01691864.2017.1405845.
[13] T. Kitago et al., “Robotic therapy for chronic stroke: General recovery of impairment or improved task-specific skill?,” J. Neurophysiol., vol. 114, no. 3, 2015, doi: 10.1152/jn.00336.2015.
[14] E. Kopperger, J. List, S. Madhira, F. Rothfischer, D. C. Lamb, and F. C. Simmel, “A self-assembled nanoscale robotic arm controlled by electric fields,” Science (80-. )., vol. 359, no. 6373, 2018, doi: 10.1126/science.aao4284.
[15] C. Laschi, M. Cianchetti, B. Mazzolai, L. Margheri, M. Follador, and P. Dario, “Soft robot arm inspired by the octopus,” Adv. Robot., vol. 26, no. 7, 2012, doi: 10.1163/156855312X626343.
[16] F. S. Lee, C. I. Lin, Z. Y. Chen, and R. X. Yang, “Development of a control architecture for a parallel three-axis robotic arm mechanism using CANopen communication protocol,” Concurr. Eng. Res. Appl., vol. 29, no. 3, 2021, doi: 10.1177/1063293X211001956.
[17] S. H. Lee et al., “Robotic Manipulation System Design and Control for Non-Contact Remote Diagnosis in Otolaryngology: Digital Twin Approach,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3259539.
[18] J. Li, A. Samoylov, J. Kim, and X. Chen, “Roman: Making Everyday Objects Robotically Manipulable with 3D-Printable Add-on Mechanisms,” in Conference on Human Factors in Computing Systems - Proceedings, 2022. doi: 10.1145/3491102.3501818.
[19] L. Liu, Q. Liu, Y. Song, B. Pang, X. Yuan, and Q. Xu, “A collaborative control method of dual-arm robots based on deep reinforcement learning,” Appl. Sci., vol. 11, no. 4, 2021, doi: 10.3390/app11041816.
[20] A. N. W. Qi, K. L. Voon, M. A. Ismail, N. Mustaffa, and M. H. Ismail, “Design and Development of a Mechanism of Robotic Arm for Lifting,” 2nd Integr. Des. Proj. Conf., no. December, 2015.
[21] F. Scotto Di Luzio et al., “Bio-cooperative approach for the human-in-the-loop control of an end-effector rehabilitation robot,” Front. Neurorobot., vol. 12, no. October, 2018, doi: 10.3389/fnbot.2018.00067.
[22] S. Shirafuji, S. Ikemoto, and K. Hosoda, “Development of a tendon-driven robotic finger for an anthropomorphic robotic hand,” Int. J. Rob. Res., vol. 33, no. 5, 2014, doi: 10.1177/0278364913518357.
[23] N. Singh, C. Huyck, V. Gandhi, and A. Jones, “Neuron-based control mechanisms for a robotic arm and hand,” Eng. Technol., vol. 11, no. 2, 2017.
[24] A. Suarez, M. Perez, G. Heredia, and A. Ollero, “Cartesian aerial manipulator with compliant arm,” Appl. Sci., vol. 11, no. 3, 2021, doi: 10.3390/app11031001.
[25] M. Tang, Y. Yan, Y. Zhang, W. Wang, and B. An, “Motion Control Of Photovoltaic Module Dust Cleaning Robotic Arm Based On Model Predictive Control,” J. Ind. Manag. Optim., vol. 19, no. 10, 2023, doi: 10.3934/jimo.2023002.
[26] K. Van Wyk, M. Culleton, J. Falco, and K. Kelly, “Comparative Peg-in-Hole Testing of a Force-Based Manipulation Controlled Robotic Hand,” IEEE Trans. Robot., vol. 34, no. 2, 2018, doi: 10.1109/TRO.2018.2791591.
[27] H. Wei, Y. Bu, and Z. Zhu, “Robotic arm controlling based on a spiking neural circuit and synaptic plasticity,” Biomed. Signal Process. Control, vol. 55, 2020, doi: 10.1016/j.bspc.2019.101640.