Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-28T07:22:40.843Z Has data issue: false hasContentIssue false

Trajectory optimization of the redundant manipulator with local variable period under multi-machine coordination

Published online by Cambridge University Press:  15 September 2022

Luchuan Yu*
Affiliation:
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China
Shunqing Zhou
Affiliation:
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China
Shenquan Huang
Affiliation:
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China
*
*Corresponding author. E-mail: yulcsdu@foxmail.com

Abstract

The coordinated motion between the press and the feeding mechanism directly determines the production efficiency of the high-speed stamping line. In order to generate the high-performance trajectory of the feeding mechanism, this paper investigates the optimization of the trajectory with the local variable period. Based on the quintic B-spline curve and normal distribution, the smooth interpolation method of variable-time interval is proposed to generate the collision-free and energy-jerk-minimal trajectory with variable-time intervals. The advantage of the proposed method is that it can make the feeding mechanism transition smoothly between districts of variable-time and fixed-time intervals. It is beneficial to avoid re-performing the entire process of trajectory planning. ADAMS and actual experiments are used to validate the effectiveness of the proposed method. Results show that the proposed method can maintain the high performance of the initial trajectory, and there is no sharp point in the displacement-time and velocity-time curves. The investigation provides a new direction for the direct generation of local variable-period trajectories in the multi-machine coordination of the stamping line.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Li, J. C., Ran, M. P. and Xie, L. H., “Efficient trajectory planning for multiple non-holonomic mobile robots via prioritized trajectory optimization,” IEEE Robot. Autom. Lett. 6(2), 405412 (2021).CrossRefGoogle Scholar
Jin, R. Y., Rocco, P. and Geng, Y. H., “Cartesian trajectory planning of space robots using a multi-objective optimization,” Aerosp Sci. Technol. 108, 106360 (2021).CrossRefGoogle Scholar
Zheng, K. M., Hu, Y. M. and Wu, B., “Trajectory planning of multi-degree-of-freedom robot with coupling effect,” J. Mech. Sci. Technol. 33(1), 413421 (2019).CrossRefGoogle Scholar
Biagiotti, L., Melchiorri, C. and Moriello, L., “Optimal trajectories for vibration reduction based on exponential filters,” IEEE Trans. Contr. Syst. Technol. 24(2), 609622 (2016).Google Scholar
Peng, X. J., Chen, G. Z., Tang, Y. J., Miao, C. W. and Li, Y., “Trajectory optimization of an electro-hydraulic robot,” J. Mech. Sci. Technol. 34(10), 42814294 (2020).Google Scholar
Liang, Y. Y., Yao, C. Z., Wu, W., Wang, L. and Wang, Q. Y., “Design and implementation of multi-axis real-time synchronous look-ahead trajectory planning algorithm,” Int. J. Adv. Manuf. Technol. 119(7-8), 49915009 (2022).CrossRefGoogle Scholar
Cappo, E. A., Desai, A., Collins, M. and Michael, N., “Online planning for human-multi-robot interactive theatrical performance,” Auton. Robot. 42(8), 17711786 (2018).CrossRefGoogle Scholar
Zhao, R. and Sidobre, D., “On-line trajectory generation considering kinematic motion constraints for robot manipulators,” Int. J. Robot. Autom. 33(6), 645653 (2018).Google Scholar
Chu, X. Y., Hu, Q. and Zhang, J. R., “Path planning and collision avoidance for a multi-arm space maneuverable robot,” IEEE Trans. Aero. Elec. Syst. 54(1), 217232 (2018).Google Scholar
Lan, J. Y., Xie, Y. G., Liu, G. J. and Cao, M. X., “A multi-objective trajectory planning method for collaborative robot,” Electronics 9(5), 859 (2020).CrossRefGoogle Scholar
Wu, G. L., Zhao, W. K. and Zhang, X. P., “Optimum time-energy-jerk trajectory planning for serial robotic manipulators by reparameterized quintic NURBS curves,” Proc. Inst. Mech. Eng. C.-J. Mech. Eng. Sci. 235(19), 43824393 (2021).CrossRefGoogle Scholar
Sathiya, V. and Chinnadurai, M., “Evolutionary algorithms-based multi-objective optimal mobile robot trajectory planning,” Robotica 37(8), 13631382 (2019).CrossRefGoogle Scholar
Gravell, B. and Summers, T., “Centralized collision-free polynomial trajectories and goal assignment for aerial swarms,” Control Eng. Pract. 109, 104753 (2021).CrossRefGoogle Scholar
Kwak, D. J., Choi, B., Cho, D., Kim, H. J. and Lee, C. W., “Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization,” Auton. Robot. 38(2), 161177 (2015).CrossRefGoogle Scholar
Kandhasamy, S., Kuppusamy, V. B. and Krishnan, S., “Scalable decentralized multi-robot trajectory optimization in continuous-time,” IEEE Access 8, 173308173322 (2020).CrossRefGoogle Scholar
Matoui, F., Boussaid, B. and Abdelkrim, M. N., “Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach,” Simul. Trans. Soc. Mod. Simul. 95(7), 637657 (2019).Google Scholar
Glorieux, E., Riazi, S. and Lennartson, B., “Productivity energy optimisation of trajectories and coordination for cyclic multi-robot systems,” Robot. Cim.-Int. Manuf. 49(4), 152161 (2018).CrossRefGoogle Scholar
Glorieux, E., Franciosa, P. and Ceglarek, D., “Quality and productivity driven trajectory optimisation for robotic handling of compliant sheet metal parts in multi-press stamping lines,” Robot. Cim.-Int. Manuf. 56(1), 264275 (2019).CrossRefGoogle Scholar
Wang, J. Y., Zhu, Y. G., Qi, R. L., Zheng, X. G. and Li, W., “Adaptive PID control of multi-DOF industrial robot based on neural network,” J. Amb. Intell. Hum. Comput. 11(12), 62496260 (2020).CrossRefGoogle Scholar
Li, X. M., “Robot target localization and interactive multi-mode motion trajectory tracking based on adaptive iterative learning,” J. Amb. Intell. Hum. Comput. 11(12), 62716282 (2020).CrossRefGoogle Scholar
Gregoire, J., Cap, M. and Frazzoli, E., “Locally-optimal multi-robot navigation under delaying disturbances using homotopy constraints,” Auton. Robot. 42(4), 895907 (2018).CrossRefGoogle Scholar
Ou, M. Y., Sun, H. B., Zhang, Z. X. and Li, L. C., “Fixed-time trajectory tracking control for multiple nonholonomic mobile robots,” Trans. Inst. Meas. Control 43(7), 15961608 (2021).CrossRefGoogle Scholar
Yu, L. C., Wang, K. Q., Zhang, Z. G., Zhang, Q. H. and Zhang, J. H., “Simulation-based multi-machine coordination for high-speed press line,” J. Braz. Soc. Mech. Sci. 41(7), 291 (2019).CrossRefGoogle Scholar
Gao, M. Y., Li, Z., He, Z. W. and Li, X., “An adaptive velocity planning method for multi-DOF robot manipulators,” Int. J. Adv. Robot. Syst. 14(3), 110 (2017).CrossRefGoogle Scholar
Yu, L. C., Wang, K. Q., Zhang, Q. H. and Zhang, J. H., “Trajectory planning of a redundant planar manipulator based on joint classification and particle swarm optimization algorithm,” Multibody Syst. Dyn. 50(1), 2543 (2020).CrossRefGoogle Scholar