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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/7/1564 |
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| author | Ruoyu Xu Chunhui Zhao Jiaxing Li Jinwen Hu Xiaolei Hou |
| author_facet | Ruoyu Xu Chunhui Zhao Jiaxing Li Jinwen Hu Xiaolei Hou |
| author_sort | Ruoyu Xu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:39:40Z |
| format | Article |
| id | doaj.art-3f600bed5aa2488692c860b6bf37f933 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T05:39:40Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-3f600bed5aa2488692c860b6bf37f9332023-11-17T16:32:25ZengMDPI AGElectronics2079-92922023-03-01127156410.3390/electronics12071564A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped RobotsRuoyu Xu0Chunhui Zhao1Jiaxing Li2Jinwen Hu3Xiaolei Hou4School of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaTraditional 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.https://www.mdpi.com/2079-9292/12/7/1564quadruped robotstrajectory planningpolynomial interpolation algorithmwhale optimization algorithmsimulated annealing algorithm |
| spellingShingle | Ruoyu Xu Chunhui Zhao Jiaxing Li Jinwen Hu Xiaolei Hou A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots Electronics quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm |
| title | A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots |
| title_full | A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots |
| title_fullStr | A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots |
| title_full_unstemmed | A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots |
| title_short | A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots |
| title_sort | hybrid improved whale optimization simulated annealing algorithm for trajectory planning of quadruped robots |
| topic | quadruped robots trajectory planning polynomial interpolation algorithm whale optimization algorithm simulated annealing algorithm |
| url | https://www.mdpi.com/2079-9292/12/7/1564 |
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