Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability
Source abstract
Abstract
This paper presents a study on optimal design to determine the installation position and link lengths of a robot within a designated workspace for welding, aiming to minimize singularities during the robot’s motion. Bi-objective functions are formulated to minimize singularities while maximizing the volumes of linear velocity manipulability ellipsoid and angular velocity manipulability ellipsoid, respectively, ensuring isotropy. We have constructed a simulation environment incorporating PID control to account for robot tracking errors. This environment was utilized as a simulator to derive a Bi-objective function set within a genetic algorithm. Through this, we optimized four robot link length variables and two installation position variables, selecting the optimal design variables on the Pareto Front. In the standard work object, the volume average of the linear velocity manipulability ellipsoid was confirmed to have improved by 72% compared to the initial level, and the isotropy of the angular velocity manipulability ellipsoid was confirmed to have improved by 23% compared to the initial level. Furthermore, correlation analysis between design parameters identified those with a high correlation with the objective functions, and the analysis results are discussed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Mechanisms and Dynamics · Manufacturing Process and Optimization · Robot Manipulation and Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, University of Pusan, Busan 46241, Republic of Korea
References (34)
Show source-provided references
1. Charkraborty, S. (2022, March 10). Designing a Ship’s Bottom Structure—A General Overview. Available online: https://www.marineinsight.com/naval-architecture/design-of-ships-bottom-structure/.
2. Wireless Teaching Pendant for Mobile Welding Robot in Shipyard10.3182/20080706-5-KR-1001.00724 ↗
3. Design of controller for mobile robot in welding process of shipbuilding engineering
4. Development of multi welding robot system for sub assembly in shipbuilding10.3182/20080706-5-KR-1001.00885 ↗
5. Mun, S., Nam, M., Lee, J., Doh, K., Park, G., Lee, H., Kim, D., and Lee, J. (2015, January 7–11). Sub-assembly welding robot system at shipyards. Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, Republic of Korea.10.1109/AIM.2015.7222754 ↗
6. Robot welding of hatch coamings for large container ships10.1108/01439910710832048 ↗
7. Programming of Welding Robots in Shipbuilding10.1016/j.procir.2021.03.107 ↗
8. Jun, B.-H., Lee, P.-M., and Lee, J. (2004, January 9–12). Manipulability analysis of underwater robotics arms on ROV and application to task-oriented joint configuration. Proceedings of the MTS/IEEE Techno-Ocean ’04 (IEEE Cat No.04CH37600), Kobe, Japan.
9. The influence of coordinates in robotic manipulability analysis10.1016/j.mechmachtheory.2019.103722 ↗
10. Yahya, S., Moghavvemi, M., and Mohamed, H.A. (2012, January 4–6). Manipulability Constraint Locus for a Six Degrees of Freedom Redundant Planar Manipulator. Proceedings of the 2012 International Symposium on Computer, Consumer and Control, Taichung, Taiwan.10.1109/IS3C.2012.79 ↗
11. Manipulability of a planar wire driven haptic device10.1016/S0094-114X(01)00076-3 ↗
12. Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks10.1109/TIE.2017.2674624 ↗
13. Menasri, R., Nakib, A., Oulhadj, H., Daachi, B., Siarry, P., and Hains, G. (2013, January 12–14). Path planning for redundant manipulators using metaheuristic for bilevel optimization and maximum of manipulability. Proceedings of the 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, China.10.1109/ROBIO.2013.6739450 ↗
14. Experimental validation of manipulability optimization control of a 7-DoF serial manipulator for robot-assisted surgery10.1002/rcs.2193 ↗
15. Jia, S., Jia, Y., and Xu, S. (2016, January 12–15). Optimization algorithm of serial manipulator structure based on posture manipulability. Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China.10.1109/WCICA.2016.7578478 ↗
16. Inverse Kinematics and Control of a 7-DOF Redundant Manipulator Based on the Closed-Loop Algorithm10.5772/10495 ↗
17. Closed-Loop Inverse Kinematics for Redundant Robots: Comparative Assessment and Two Enhancements10.1109/TMECH.2014.2326304 ↗
18. Chan, T.F., and Dubey, R.V. (1993, January 2–6). A weighted least-norm solution based scheme for avoiding joint limits for redundant manipulators. Proceedings of the 1993 Proceedings IEEE International Conference on Robotics and Automation, Atlanta, GA, USA.
19. PID controller with novel PSO applied to a joint of a robotic manipulator10.1007/s40430-021-03092-4 ↗
20. A parallel learning particle swarm optimizer for inverse kinematics of robotic manipulator10.1002/int.22543 ↗
21. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley.
22. Ngatchou, P., Zarei, A., and El-Sharkawi, A. (2005, January 6–10). Pareto Multi Objective Optimization. Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, Arlington, VA, USA.
23. Raunek (2022, March 10). A Guide to Types of Ships. Available online: https://www.marineinsight.com/guidelines/a-guide-to-types-of-ships/.
24. Miller (2022, May 05). What Are the 4 Basic Welding Positions and When Should You Use Them?. Available online: https://www.millerwelds.com/resources/article-library/what-are-the-4-basic-welding-positions-and-when-should-you-use-them/.
25. MachineMFG (2022, May 05). What Is the Effect of Welding Direction and Angle on Weld Formation. Available online: https://www.machinemfg.com/effect-of-welding-direction-and-angle-on-weld-formation/.
26. (2022, June 02). 3D Rotations. Available online: http://motion.pratt.duke.edu/RoboticsSystems/3DRotations.html.
27. Gao, F., Chen, Q., and Guo, L. (2015, January 29–31). Study on arc welding robot weld seam touch sensing location method for structural parts of hull. Proceedings of the 2015 International Conference on Control, Automation and Information Sciences (ICCAIS), Changshu, China.
28. Granja, M., Chang, N., Granja, V., Duque, M., and Llulluna, F. (2016, January 22–23). Comparison between Standard and Modified Denavit-Hartenberg Method in Robotics Modeling. Proceedings of the 2nd World Congress on Mechanical, Chemical, and Material Engineering (MCM’16), Budapest, Hungary. Paper No. ICME 118.10.11159/icmie16.118 ↗
29. Jin, S., Lee, S.K., Lee, J., and Han, S. (2019). Kinematic Model and Real-Time Path Generator for a Wire-Driven Surgical Robot Arm with Articulated Joint Structure. Appl. Sci., 9.10.3390/app9194114 ↗
30. Spong, M.W., Hutchinson, S., and Vidyasagar, M. (2020). Robot Modeling and Control, John Wiley & Sons, Inc.. [1st ed.].
31. Manipulability of Robotic Mechanisms10.1177/027836498500400201 ↗
32. Long, P., and Padir, T. (2018, January 6–9). Evaluating Robot Manipulability in Constrained Environments by Velocity Polytope Reduction. Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China.10.1109/HUMANOIDS.2018.8624962 ↗
33. An Algorithm for Convex Polytopes10.1145/321556.321564 ↗
34. Genetic Algorithm: An Efficient Tool for Global Optimization