Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-25T02:07:27.222Z Has data issue: false hasContentIssue false
  • English
  • Français

An optimal trajectory planning method for path tracking of industrial robots

Published online by Cambridge University Press:  30 October 2018

Xianxi Luo ,
Shuhui Li ,
Shubo Liu  and
Guoquan Liu
Xianxi Luo*
Affiliation:
Jiangxi Engineering Research Center for New Energy Technology and Equipment, East China University of Technology, Nanchang 330013, China
Shuhui Li
Affiliation:
Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa 35401-1956, USA
Shubo Liu
Affiliation:
Jiangxi Engineering Research Center for New Energy Technology and Equipment, East China University of Technology, Nanchang 330013, China
Guoquan Liu
Affiliation:
Jiangxi Engineering Research Center for New Energy Technology and Equipment, East China University of Technology, Nanchang 330013, China
*
*Corresponding author. E-mail: xianxi_luo@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents an optimal trajectory planning method for industrial robots. The paper specially focuses on the applications of path tracking. The problem is to plan the trajectory with a specified geometric path, while allowing the position and orientation of the path to be arbitrarily selected within the specific ranges. The special contributions of the paper include (1) an optimal path tracking formulation focusing on the least time and energy consumption without violating the kinematic constraints, (2) a special mechanism to discretize a prescribed path integration for segment interpolation to fulfill the optimization requirements of a task with its constraints, (3) a novel genetic algorithm (GA) optimization approach that transforms a target path to be tracked as a curve with optimal translation and orientation with respect to the world Cartesian coordinate frame, (4) an integration of the interval analysis, piecewise planning and GA algorithm to overcome the challenges for solving the special trajectory planning and path tracking optimization problem. Simulation study shows that it is an insufficient condition to define a trajectory just based on the consideration that each point on the trajectory should be reachable. Simulation results also demonstrate that the optimal trajectory for a path tracking problem can be obtained effectively and efficiently using the proposed method. The proposed method has the properties of broad adaptability, high feasibility and capability to achieve global optimization.

Keywords

Industrial robotTrajectory planningGenetic algorithmWork piece placement
Type
Articles
Information
Robotica , Volume 37 , Issue 3 , March 2019 , pp. 502 - 520
Copyright
Copyright © Cambridge University Press 2018 

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

1. Li, Z., Li, G. and Sun, Y., “Development of articulated robot trajectory planning,” Int. J. Comput. Sci. Math. 8 (1), 52–60 (2017).Google Scholar
2. Yildirim, S. and Ulu, B., “Design of Artificial Neural Network Predictor for Trajectory Planning of an Experimental 6 DOF Robot Manipulator,” Proceedings of the 4th Conference on Mechanisms, Transmissions and Applications MeTrApp2017, Springer, Trabzon, Turkey, Netherlands (Jul. 3, 2017–Jul. 5, 2017).Google Scholar
3. Le, J., Zhang, H. and Chen, X., “Realization of rectangular fillet weld tracking based on rotating arc sensors and analysis of experimental results in gas metal arc welding,” Robot. Comput.-Integrated Manuf. 49, 263–276 (2018).Google Scholar
4. Jingwei, L., Yifei, T. and Shaofeng, W., “Welding robot kinematics analysis and trajectory planning,” Telkomnika (Telecommun. Comput. Electron. Control) 14 (2A), 92–100 (2016).Google Scholar
5. Zou, Q., Guo, W. and Hamimid, F. Y., “A Novel Robot Trajectory Planning Algorithm Based on NURBS Velocity Adaptive Interpolation,” Proceedings of the International Conference on Mechanical Design ICMD2017, Springer, Beijing, China, Netherlands (Oct. 13, 2017–Oct. 15, 2017).Google Scholar
6. Chen, Y. D. et al., “Optimal trajectory planning for industrial robots using harmony search algorithm,” Ind. Robot-an Int. J. 40 (5), 502–512 (2013).Google Scholar
7. Rubio, F. et al., “Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory,” Robot. Autonomous Syst. 86, 106–112 (2016).Google Scholar
8. Bu, J. and Xu, J., “Research on time optimal trajectory planning of 7-DOF manipulator based on genetic algorithm,” Acta Technica CSAV (Ceskoslovensk Akademie Ved) 61 (4), 189–199 (2016).Google Scholar
9. Xidias, E. K., “Time-optimal trajectory planning for hyper-redundant manipulators in 3D workspaces,” Robot. Comput.-Integrated Manuf. 50, 286–298 (2018).Google Scholar
10. Liu, H. S., Lai, X. B. and Wu, W. X., “Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints,” Robot. Comput.-Integrated Manuf. 29 (2), 309–317 (2013).Google Scholar
11. Huang, J. et al., “Optimal time-jerk trajectory planning for industrial robots,” Mechanism Mach. Theory 121, 530–544 (2018).Google Scholar
12. Luo, L. P. et al., “Trajectory planning for energy minimization of industry robotic manipulators using the Lagrange interpolation method,” Int. J. Precision Eng. Manuf. 16 (5), 911–917 (2015).Google Scholar
13. Liu, Y. et al., “A method of energy-optimal trajectory planning for palletizing robot,” Math. Problems Eng. 2017, 1–10 (2017).Google Scholar
14. Tao, J. et al., “Genetic algorithm based homing trajectory planning of parafoil system with constraints,” Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) 48 (2), 404–410 (2017).Google Scholar
15. Lin, H. I., “A fast and unified method to find a minimum-jerk robot joint trajectory using particle swarm optimization,” J. Intell. Robot. Syst. 75 (3–4), 379–392 (2014).Google Scholar
16. Gao, X. S., Mu, Y. and Gao, Y. Z., “Optimal trajectory planning for robotic manipulators using improved teaching-learning-based optimization algorithm,” Ind. Robot-an Int. J. 43 (3), 308–316 (2016).Google Scholar
17. Zanchettin, A. M. and Rocco, P., “Motion planning for robotic manipulators using robust constrained control,” Control Eng. Practice 59, 127–136 (2017).Google Scholar
18. Munasinghe, S. R., “Trajectory planning and control of industrial robot manipulators,” In: Industrial Robotics: Theory, Modelling and Control (Sam Cubero, ed.) (Intech, 2006) pp. 335–348.Google Scholar
19. Gao, M. et al., “Adaptive Velocity Planning for 6-DOF Robots with Fixed Tracks,” Proceedings of the 25th IEEE International Symposium on Industrial Electronics ISIE2016, Institute of Electrical and Electronics Engineers Inc, Santa Clara, CA, USA (Jun. 8, 2016–Jun. 10, 2016).Google Scholar
20. Chen, K. et al., “A Path-Planning Algorithm of the Automatic Welding Robot System for Three-Dimensional Arc Welding using Image Processing,” Proceedings of the 13th International Conference on Ubiquitous Robots and Ambient Intelligence URAI2016, Institute of Electrical and Electronics Engineers Inc, Xian, China (Aug. 19, 2016–Aug. 22, 2016).Google Scholar
21. Corke, P., Robotics, Vision and Control (Springer, Berlin, Heidelberg, 2011).Google Scholar
22. Lin, C., Chang, P. and Luh, J., “Formulation and optimization of cubic polynomial joint trajectories for industrial robots,” IEEE Trans. Autom. Control 28 (12), 1066–1074 (1983).Google Scholar
23. Tse, K. and Wang, C., “Evolutionary optimization of cubic polynomial joint trajectories for industrial robots,” Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA (1998).Google Scholar
24. Chettibi, T. et al., “Minimum cost trajectory planning for industrial robots,” Eur. J. Mech. a-Solids 23 (4), 703–715 (2004).Google Scholar