Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T09:33:30.176Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-objective optimal trajectory planning for manipulators in the presence of obstacles

Published online by Cambridge University Press:  09 July 2021

Xiaofu Zhang Open the ORCID record for Xiaofu Zhang [Opens in a new window]  and
Guanglin Shi Open the ORCID record for Guanglin Shi [Opens in a new window]
Xiaofu Zhang
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Guanglin Shi*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a trajectory planning method based on multi-objective optimization, including time optimal and jerk optimal for the manipulators in the presence of obstacles. The proposed method generates a trajectory configuration in the joint space with kinematic and obstacle constraints using quintic B-spline. Gilbert–Johnson–Keerthi detecting algorithm is utilized to detect whether there is a collision and obtain the minimum distance between the manipulator and obstacles. The degree of constraint violations is introduced to redefine the Pareto domination, and the constrained multi-objective particle swarm algorithm (CMOPSO) is adopted to solve the time-jerk optimization problem. Finally, the Z-type fuzzy membership function is proposed to select the best optimal solution in the Pareto front obtained by CMOPSO. Test results show the effectiveness of the proposed method.

Keywords

trajectory planningobstacle avoidancemulti-objective particle swarm optimizationQuintic B-spline
Type
Research Article
Information
Robotica , Volume 40 , Issue 4 , April 2022 , pp. 888 - 906
Copyright
© The Author(s), 2021. 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

Chettibi, T., “Smooth point-to-point trajectory planning for robot manipulators by using radial basis functions,” Robotica. 37(3), 539–559 (2019).CrossRefGoogle Scholar
Gregory, J., Alberto, O. and Ernesto, S., “Energy-optimal trajectory planning for robot manipulators with holonomic constraints,” Syst Control Lett. 61(2), 279–291 (2012).CrossRefGoogle Scholar
Abe, A., “Trajectory planning for flexible Cartesian robot manipulator by using artificial neural network: Numerical simulation and experimental verification,” Robotica. 29(5), 797–804 (2011).CrossRefGoogle Scholar
Gasparetto, A., Boscariol, P., Lanzutti, A. and Vidoni, R., “Path planning and trajectory planning algorithms: A general overview,” In: Motion and Operation Planning of Robotic Systems. Mechanisms and Machine Science (Carbone, G. and Gomez-Bravo, F., eds.) vol. 29, pp. 3–27 (Springer, Cham, 2015).Google Scholar
Boryga, M. and Graboś, A., “Planning of manipulator motion trajectory with higher-degree polynomials use,” Mech Mach Theory. 44(7), 1400–1419 (2009).CrossRefGoogle Scholar
Ho, C. Y. and Cook, C. C., “The application of spline functions to trajectory generation for computer controlled manipulators,” In: Computing Techniques for Robots (Aleksander, I., ed.) vol. 1, pp. 325–333. (Springer, Boston, 1982).Google Scholar
Boscariol, P., Gasparetto, A. and Vidoni, R., “Jerk-continuous trajectories for cyclic tasks,” In: Proceedings of the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2012), pp. 1277–1286.Google Scholar
Barghi Jond, H., Nabiyev, V. V, and Çavdar, T., “Trajectory planning for point-to-point motion using high-order polynomials,” M J Ele Eng. 14(1), 1–8 (2020).Google Scholar
Saramago, S. F. P. and Junior, V. S., “Optimal trajectory planning of robot manipulators in the presence of moving obstacles,” Mech Mach Theory. 35(8), 1079–1094 (2000).CrossRefGoogle Scholar
Gasparetto, A. and Zanotto, V., “A new method for smooth trajectory planning of robot manipulators,” Mech Mach Theory. 42(4), 455–471 (2007).CrossRefGoogle Scholar
Bobrow, J. E., Dubowsky, S. and Gibson, J. S., “Time-optimal control of robotic manipulators along specified paths,” Int J Robot Res. 4(3), 554–561 (1985).CrossRefGoogle Scholar
Shin, K. and McKay, N., “A dynamic programming approach to trajectory planning of robotic manipulators,” IEEE Trans Autom Control. 31(6), 491–500 (1986).CrossRefGoogle Scholar
Verscheure, D., Demeulenaere, B., Swevers, J., Schutter, J. D. and Diehl, M., “Time-energy optimal path tracking for robots: A numerically efficient optimization approach,” In: Proceedings of the 10th International Workshop on Advanced Motion Control (2008), pp. 727–732.Google Scholar
Reiter, A. and Müller, A., “On higher order inverse kinematics methods in time-optimal trajectory planning for kinematically redundant manipulators,” IEEE T Ind Inform. 14(4), 1681–1690 (2018).CrossRefGoogle Scholar
Gasparetto, A. and Zanotto, V., “A technique for time-jerk optimal planning of robot trajectories,” Robot Comput-Integr Manuf. 24(3), 415–426 (2008).CrossRefGoogle Scholar
Zanotto, V., Gasparetto, A., Lanzutti, A., Boscariol, P. and Vidoni, R., “Experimental validation of minimum time-jerk algorithms for industrial robots,” J Intell Robot Syst. 64(2), 197–219 (2011).CrossRefGoogle Scholar
Solteiro Pires, E. J., Tenreiro Machado, J. A. and de Moura Oliveira, P. B., “Robot trajectory planning using multi-objective genetic algorithm optimization,” In: Proceedings of Genetic and Evolutionary Computation Conference (2004), pp. 615–626.Google Scholar
Huang, J., Hu, P., Wu, K. and Zeng, M., “Optimal time-jerk trajectory planning for industrial robots,” Mech Mach Theory. 121, 530–544 (2018).CrossRefGoogle Scholar
Abu-Dakka, F. J., Valero, F. J., Suner, J. L. and Mata, V., “A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments,” Robotica. 33(3), 669–683 (2018).CrossRefGoogle Scholar
Shi, X. G., Fang, H. G. and Guo, L. J.. “Multi-objective optimal trajectory planning of manipulators based on quintic NURBS,” In: Proceedings of IEEE International Conference on Mechatronics and Automation (2016), pp. 759–765.Google Scholar
Chen, D., Li, S. Q., Wang, J. F., Feng, Y. and Liu, Y., “A multi-objective trajectory planning method based on the improved immune clonal selection algorithm,” Robot Comput Integr Manuf. 59, 431–442 (2019).CrossRefGoogle Scholar
Rout, A., Mahanta, G. B., Bbvl, D. and Biswal, B. B.. “Kinematic and dynamic optimal trajectory planning of industrial robot using improved multi-objective ant lion optimizer,” J Inst Eng India Ser C. 101(3), 559–569 (2020).CrossRefGoogle Scholar
Huang, P., Liu, G., Yuan, J. and Xu, Y., “Multi-objective optimal trajectory planning of space robot using particle swarm optimization,” In: Proceedings of International Symposium on Neural Networks (2008), pp. 171–179.Google Scholar
Xu, Z., Li, S., Chen, Q. and Hou, B., “MOPSO based multi-objective trajectory planning for robot manipulators,” In: Proceedings of International Conference on Information Science and Control Engineering (2015), pp. 824–828.Google Scholar
Souzanchi-K, M., Aliasghar, A., Mohammad-R, A. and Fateh, M. M.. “Robust impedance control of uncertain mobile manipulators using time-delay compensation,” IEEE Trans Control Syst Technol. 26(6), 1942–1953 (2017).CrossRefGoogle Scholar
Jalaeian-F, M., Fateh, M. M. and Rahimiyan, M., “Bi-level adaptive computed-current impedance controller for electrically driven robots,” Robotica. 39(2), 200–216 (2021).CrossRefGoogle Scholar
Reyes-Sierra, M. and Coello, C. A. C., “Multi-objective particle swarm optimizers: A survey of the state-of-the-art,” Int J Comput Intell Res. 2(3), 287–308 (2006). ISSN 0973-1873.Google Scholar
De Boor, C., A Practical Guide to Splines (Springer Verlag. New York, US, 1978).CrossRefGoogle Scholar
Grandine, T. A. and Klein, F. W., “A new approach to the surface intersection problem,” Comput Aided Geom Des. 14(2), 111–134 (1997).CrossRefGoogle Scholar
Coello, C. A. C., Pulido, G. T. and Lechuga, M. S., “Handling multiple objectives with particle swarm optimization,” IEEE Trans Evol Comput. 8(3), 256–279 (2004).CrossRefGoogle Scholar
Bastos-Filho, C. J. A. and Miranda, P. B. C., “Multi-objective particle swarm optimization using speciation,” In: Proceedings of IEEE Symposium on Swarm Intelligence conference (2011), pp. 1–6.Google Scholar
Goldberg, D. E. and Deb, K., “A comparative analysis of selection schemes used in genetic algorithms,” Found Genet Algorithms. 1, 69–93 (1991).Google Scholar
Ali, M., Siarry, P. and Pant, M., “An efficient differential evolution based algorithm for solving multi-objective optimization problems,” Eur J Oper Res. 217(2), 404–416 (2012).Google Scholar
Saravanan, R., Ramabalan, S. and Balamurugan, C., “Evolutionary multi-criteria trajectory modelling of industrial robots in the presence of obstacles,” Eng Appl Artif Intell. 22(2), 329–342 (2009).CrossRefGoogle Scholar