Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators

Juan Du; Jie Hou; Heyang Wang; Zhi Chen; Juan Du; Jie Hou; Heyang Wang; Zhi Chen

doi:10.3934/mbe.2023728

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 16304-16329. doi: 10.3934/mbe.2023728

Previous Article Next Article

Research article

Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators

School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, China

Academic Editor: Yang Kuang

Received: 18 June 2023 Revised: 26 July 2023 Accepted: 04 August 2023 Published: 14 August 2023

To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.
- trajectory planning,
- time-optimal,
- whale optimization algorithm,
- reinforcement learning,
- VNS algorithm
Citation: Juan Du, Jie Hou, Heyang Wang, Zhi Chen. Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16304-16329. doi: 10.3934/mbe.2023728

Related Papers:

Abstract

To address the issues of unstable, non-uniform and inefficient motion trajectories in traditional manipulator systems, this paper proposes an improved whale optimization algorithm for time-optimal trajectory planning. First, an inertia weight factor is introduced into the surrounding prey and bubble-net attack formulas of the whale optimization algorithm. The value is controlled using reinforcement learning techniques to enhance the global search capability of the algorithm. Additionally, the variable neighborhood search algorithm is incorporated to improve the local optimization capability. The proposed whale optimization algorithm is compared with several commonly used optimization algorithms, demonstrating its superior performance. Finally, the proposed whale optimization algorithm is employed for trajectory planning and is shown to be able to produce smooth and continuous manipulation trajectories and achieve higher work efficiency.

References

[1]	L. Wang, Q. Wu, F. Lin, S. Li, D. Chen, A new trajectory-planning beetle swarm optimization algorithm for trajectory planning of robot manipulators, IEEE Access, 7 (2019), 154331–154345. https://doi.org/10.1109/ACCESS.2019.2949271 doi: 10.1109/ACCESS.2019.2949271
[2]	H. Zhao, B. Zhang, L. Yang, J. Sun, Z. Gao, Obstacle avoidance and near time-optimal trajectory planning of a robotic manipulator based on an improved whale optimization algorithm, Arab. J. Sci. Eng., 47 (2022), 16421–16438. https://doi.org/10.1007/s13369-022-06926-y doi: 10.1007/s13369-022-06926-y
[3]	S. Jia, J. Shan, Finite-time trajectory tracking control of space manipulator under actuator saturation, IEEE Trans. Ind. Electron., 67 (2020), 2086–2096. https://doi.org/10.1109/TIE.2019.2902789 doi: 10.1109/TIE.2019.2902789
[4]	T. Zhang, M. Zhang, Y. Zou, Time-optimal and smooth trajectory planning for robot manipulators, Int. J. Control Autom. Syst., 19 (2021), 521–531. https://doi.org/10.1007/s12555-019-0703-3 doi: 10.1007/s12555-019-0703-3
[5]	A. Abe, Minimum energy trajectory planning method for robot manipulator mounted on flexible base, in 2013 9th Asian Control Conference (ASCC), (2013), 1–7. https://doi.org/10.1109/ASCC.2013.6606088
[6]	D. Chen, Y. Zhang, Minimum jerk norm scheme applied to obstacle avoidance of redundant robot arm with jerk bounded and feedback control, IET Control Theory Appl., 10 (2016), 1896–1903. https://doi.org/10.1049/iet-cta.2016.0220 doi: 10.1049/iet-cta.2016.0220
[7]	X. Zhang, G. Shi, Multi-objective optimal trajectory planning for manipulators in the pre-sence of obstacles, Robotica, 40 (2021), 1–19. https://doi.org/10.1017/S0263574721000886 doi: 10.1017/S0263574721000886
[8]	J. Liu, H. Wang, X. Li, K. Chen, C. Li, Robotic arm trajectory optimization based on multiverse algorithm, Math. Biosci. Eng., 20 (2023), 2776–2792. https://doi.org/10.3934/mbe.2023130 doi: 10.3934/mbe.2023130
[9]	L. Zhang, Y. Wang, X. Zhao, P. Zhao, L. He, Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm, J. Mech. Sci. Technol., 35 (2021), 3171–3181. https://doi.org/10.1007/s12206-021-0638-5 doi: 10.1007/s12206-021-0638-5
[10]	X. Gao, Y. Mu, Y. Gao, Optimal trajectory planning for robotic manipulators using impr-oved teaching-learning-based optimization algorithm, Ind. Robot, 43 (2016), 308–316. https://doi.org/10.1108/IR-08-2015-0167 doi: 10.1108/IR-08-2015-0167
[11]	Y. Du, Y. Chen, Time optimal trajectory planning algorithm for robotic manipulator based on locally chaotic particle swarm optimization, Chin. J. Electron., 31 (2022), 906–914. https://doi.org/10.1049/cje.2021.00.373 doi: 10.1049/cje.2021.00.373
[12]	X. Zhang, F. Xiao, X. Tong, J. Yun, Y. Liu, Y. Sun, et al., Time optimal trajectory planning based on improved sparrow search algorithm, Front. Bioeng. Biotechnol., 10 (2022).https://doi.org/10.3389/fbioe.2022.852408 doi: 10.3389/fbioe.2022.852408
[13]	L. Sun, J. Huang, J. Xu, Y. Ma, Feature selection based on adaptive whale optimization algorithm and fault-tolerance neighborhood rough sets, Pattern Recognit. Artif. Intell., 35 (2022), 150–165. https://doi.org/10.16451/j.cnki.issn1003-6059.202202006 doi: 10.16451/j.cnki.issn1003-6059.202202006
[14]	W. Yang, K. Xia, S. Fan, L. Wang, T. Li, J. Zhang, et al., A multi-strategy whale optimization algorithm and its application, Eng. Appl. Artif. Intell., 108 (2022), 104558. https://doi.org/10.1016/j.engappai.2021.104558 doi: 10.1016/j.engappai.2021.104558
[15]	J. Anitha, S. I. A. Pandian, S. A. Agnes, An efficient multilevel color image thresholding based on modified whale optimization algorithm, Expert Syst. Appl., 178 (2021), 115003. https://doi.org/10.1016/j.eswa.2021.115003 doi: 10.1016/j.eswa.2021.115003
[16]	L. Piegl, W. Tiller, The NURBS Book, Springer Science & Business Media, (1996).
[17]	X. Li, H. Zhao, X. He, H. Ding, A novel cartesian trajectory planning method by using triple NURBS curves for industrial robots, Robot Comput. Integr. Manuf., 83 (2023), 102576. https://doi.org/10.1016/j.rcim.2023.102576 doi: 10.1016/j.rcim.2023.102576
[18]	W. Ma, T. Hu, C. Zhang, T. Zhang, A robot motion position and posture control method for freeform surface laser treatment based on NURBS interpolation, Robot Comput. Integr. Manuf., 83 (2023), 102547. https://doi.org/10.1016/j.rcim.2023.102547 doi: 10.1016/j.rcim.2023.102547
[19]	S. Li, X. Zhang, Research on planning and optimization of trajectory for underwater vision welding robot, Array, 16 (2022), 100253. https://doi.org/10.1016/j.array.2022.100253 doi: 10.1016/j.array.2022.100253
[20]	W. Wang, Q. Wang, R. Zhong, L. Chen, X. Shi, Stacking sequence optimization of arbitrary quadrilateral laminated plates for maximum fundamental frequency by hybrid whale optimization algorithm, Compos. Struct., 310 (2023), 116764. https://doi.org/10.1016/j.compstruct.2023.116764 doi: 10.1016/j.compstruct.2023.116764
[21]	L. P. Kaelbling, M. L. Littman, A. W. Moore, Reinforcement learning: A Survey, J. Artif. Intell. Res., 4 (1996), 237–285. https://doi.org/10.1613/jair.301 doi: 10.1613/jair.301
[22]	M. Fayyazi, M. Abdoos, D. Phan, M. Golafrouz, M. Jalili, R. N. Jazar, et al., Real-time self-adaptive Q-learning controller for energy management of conventional autonomous vehicles, Expert Syst. Appl., 222 (2023), 119770. https://doi.org/10.1016/j.eswa.2023.119770 doi: 10.1016/j.eswa.2023.119770
[23]	R. Chen, B. Yang, S. Li, S. Wang, A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem, Comput. Ind. Eng., 149 (2020), 106778. https://doi.org/10.1016/j.cie.2020.106778 doi: 10.1016/j.cie.2020.106778
[24]	V. Helder, T. Filomena, L. Ferreira, G. Kirch, Application of the VNS heuristic for feat-ure selection in credit scoring problems, Mach. Learn. Appl., 9 (2022), 100349. https://doi.org/10.1016/j.mlwa.2022.100349 doi: 10.1016/j.mlwa.2022.100349
[25]	R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, (2018).
[26]	S. Chakraborty, S. Sharma, A. K. Saha, A. Saha, A novel improved whale optimization algorithm to solve numerical optimization and real-world applications, Artif. Intell. Rev., 55 (2022), 4605–4716. https://doi.org/10.1007/s10462-021-10114-z doi: 10.1007/s10462-021-10114-z
[27]	L. Abualigah, M. A. Elaziz, P. Sumari, Z. W. Geem, A. H. Gandomi, Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer, Expert Syst. Appl., 191 (2022), 116158. https://doi.org/10.1016/j.eswa.2021.116158 doi: 10.1016/j.eswa.2021.116158
[28]	F. A. Hashim, A. G. Hussien, Snake optimizer: A novel meta-heuristic optimization algorithm, Knowl. Based Syst., 242 (2022), 108320. https://doi.org/10.1016/j.knosys.2022.108320 doi: 10.1016/j.knosys.2022.108320

mbe-20-09-728-supplementary.pdf

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)