A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots

Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based...
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Autor*in:	Ruoyu Xu [verfasserIn] Chunhui Zhao [verfasserIn] Jiaxing Li [verfasserIn] Jinwen Hu [verfasserIn] Xiaolei Hou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 12(2023), 7, p 1564
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:7, p 1564

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics12071564

Katalog-ID:	DOAJ089388402

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520			\|a Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm.
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allfields	10.3390/electronics12071564 doi (DE-627)DOAJ089388402 (DE-599)DOAJ3f600bed5aa2488692c860b6bf37f933 DE-627 ger DE-627 rakwb eng TK7800-8360 Ruoyu Xu verfasserin aut A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm. quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm Electronics Chunhui Zhao verfasserin aut Jiaxing Li verfasserin aut Jinwen Hu verfasserin aut Xiaolei Hou verfasserin aut In Electronics MDPI AG, 2013 12(2023), 7, p 1564 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:7, p 1564 https://doi.org/10.3390/electronics12071564 kostenfrei https://doaj.org/article/3f600bed5aa2488692c860b6bf37f933 kostenfrei https://www.mdpi.com/2079-9292/12/7/1564 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 7, p 1564
spelling	10.3390/electronics12071564 doi (DE-627)DOAJ089388402 (DE-599)DOAJ3f600bed5aa2488692c860b6bf37f933 DE-627 ger DE-627 rakwb eng TK7800-8360 Ruoyu Xu verfasserin aut A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm. quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm Electronics Chunhui Zhao verfasserin aut Jiaxing Li verfasserin aut Jinwen Hu verfasserin aut Xiaolei Hou verfasserin aut In Electronics MDPI AG, 2013 12(2023), 7, p 1564 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:7, p 1564 https://doi.org/10.3390/electronics12071564 kostenfrei https://doaj.org/article/3f600bed5aa2488692c860b6bf37f933 kostenfrei https://www.mdpi.com/2079-9292/12/7/1564 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 7, p 1564
allfields_unstemmed	10.3390/electronics12071564 doi (DE-627)DOAJ089388402 (DE-599)DOAJ3f600bed5aa2488692c860b6bf37f933 DE-627 ger DE-627 rakwb eng TK7800-8360 Ruoyu Xu verfasserin aut A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm. quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm Electronics Chunhui Zhao verfasserin aut Jiaxing Li verfasserin aut Jinwen Hu verfasserin aut Xiaolei Hou verfasserin aut In Electronics MDPI AG, 2013 12(2023), 7, p 1564 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:7, p 1564 https://doi.org/10.3390/electronics12071564 kostenfrei https://doaj.org/article/3f600bed5aa2488692c860b6bf37f933 kostenfrei https://www.mdpi.com/2079-9292/12/7/1564 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 7, p 1564
allfieldsGer	10.3390/electronics12071564 doi (DE-627)DOAJ089388402 (DE-599)DOAJ3f600bed5aa2488692c860b6bf37f933 DE-627 ger DE-627 rakwb eng TK7800-8360 Ruoyu Xu verfasserin aut A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm. quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm Electronics Chunhui Zhao verfasserin aut Jiaxing Li verfasserin aut Jinwen Hu verfasserin aut Xiaolei Hou verfasserin aut In Electronics MDPI AG, 2013 12(2023), 7, p 1564 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:7, p 1564 https://doi.org/10.3390/electronics12071564 kostenfrei https://doaj.org/article/3f600bed5aa2488692c860b6bf37f933 kostenfrei https://www.mdpi.com/2079-9292/12/7/1564 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 7, p 1564
allfieldsSound	10.3390/electronics12071564 doi (DE-627)DOAJ089388402 (DE-599)DOAJ3f600bed5aa2488692c860b6bf37f933 DE-627 ger DE-627 rakwb eng TK7800-8360 Ruoyu Xu verfasserin aut A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm. quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm Electronics Chunhui Zhao verfasserin aut Jiaxing Li verfasserin aut Jinwen Hu verfasserin aut Xiaolei Hou verfasserin aut In Electronics MDPI AG, 2013 12(2023), 7, p 1564 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:7, p 1564 https://doi.org/10.3390/electronics12071564 kostenfrei https://doaj.org/article/3f600bed5aa2488692c860b6bf37f933 kostenfrei https://www.mdpi.com/2079-9292/12/7/1564 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 7, p 1564
language	English
source	In Electronics 12(2023), 7, p 1564 volume:12 year:2023 number:7, p 1564
sourceStr	In Electronics 12(2023), 7, p 1564 volume:12 year:2023 number:7, p 1564
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm Electronics
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container_title	Electronics
authorswithroles_txt_mv	Ruoyu Xu @@aut@@ Chunhui Zhao @@aut@@ Jiaxing Li @@aut@@ Jinwen Hu @@aut@@ Xiaolei Hou @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Ruoyu Xu
spellingShingle	Ruoyu Xu misc TK7800-8360 misc quadruped robots misc trajectory planning misc polynomial interpolation algorithm misc whale optimization algorithm misc simulated annealing algorithm misc Electronics A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots
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topic_title	TK7800-8360 A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm
topic	misc TK7800-8360 misc quadruped robots misc trajectory planning misc polynomial interpolation algorithm misc whale optimization algorithm misc simulated annealing algorithm misc Electronics
topic_unstemmed	misc TK7800-8360 misc quadruped robots misc trajectory planning misc polynomial interpolation algorithm misc whale optimization algorithm misc simulated annealing algorithm misc Electronics
topic_browse	misc TK7800-8360 misc quadruped robots misc trajectory planning misc polynomial interpolation algorithm misc whale optimization algorithm misc simulated annealing algorithm misc Electronics
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title	A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots
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title_full	A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots
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title_sort	hybrid improved-whale-optimization–simulated-annealing algorithm for trajectory planning of quadruped robots
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title_auth	A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots
abstract	Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm.
abstractGer	Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm.
abstract_unstemmed	Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm.
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title_short	A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots
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A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots

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