Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-08-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023728?viewType=HTML |
| _version_ | 1797672860858187776 |
|---|---|
| author | Juan Du Jie Hou Heyang Wang Zhi Chen |
| author_facet | Juan Du Jie Hou Heyang Wang Zhi Chen |
| author_sort | Juan Du |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T21:36:20Z |
| format | Article |
| id | doaj.art-f766183cc6aa4f2fb699383f8f0e6917 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-11T21:36:20Z |
| publishDate | 2023-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-f766183cc6aa4f2fb699383f8f0e69172023-09-27T01:39:12ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-08-01209163041632910.3934/mbe.2023728Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulatorsJuan Du0Jie Hou1Heyang Wang2Zhi Chen3School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, ChinaSchool of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, ChinaSchool of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, ChinaSchool of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyaun 030024, ChinaTo 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.https://www.aimspress.com/article/doi/10.3934/mbe.2023728?viewType=HTMLtrajectory planningtime-optimalwhale optimization algorithmreinforcement learningvns algorithm |
| spellingShingle | Juan Du Jie Hou Heyang Wang Zhi Chen Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators Mathematical Biosciences and Engineering trajectory planning time-optimal whale optimization algorithm reinforcement learning vns algorithm |
| title | Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators |
| title_full | Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators |
| title_fullStr | Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators |
| title_full_unstemmed | Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators |
| title_short | Application of an improved whale optimization algorithm in time-optimal trajectory planning for manipulators |
| title_sort | application of an improved whale optimization algorithm in time optimal trajectory planning for manipulators |
| topic | trajectory planning time-optimal whale optimization algorithm reinforcement learning vns algorithm |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023728?viewType=HTML |
| work_keys_str_mv | AT juandu applicationofanimprovedwhaleoptimizationalgorithmintimeoptimaltrajectoryplanningformanipulators AT jiehou applicationofanimprovedwhaleoptimizationalgorithmintimeoptimaltrajectoryplanningformanipulators AT heyangwang applicationofanimprovedwhaleoptimizationalgorithmintimeoptimaltrajectoryplanningformanipulators AT zhichen applicationofanimprovedwhaleoptimizationalgorithmintimeoptimaltrajectoryplanningformanipulators |