JOURNAL ARTICLE
Optimization of collaborative robot motion trajectory based on energy consumption model.
Published In: International Journal of Modeling, Simulation & Scientific Computing, 2026, v. 17, n. 2. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Wang, Dianjun; Sun, Weidong; Chen, Ya; Wang, Zilong; Wang, Peng; Song, Zhongkang 3 of 3
Abstract
To enhance the dynamic performance and reduce energy consumption of collaborative robots, a motion trajectory optimization method based on an energy consumption model is proposed. By establishing a dynamic model of the entire collaborative robot and analyzing its energy consumption characteristics during operation, a joint energy consumption integrated model and a complete machine energy consumption model are constructed. A trajectory optimization method for collaborative robots is proposed, with energy consumption as the optimization objective and time and speed as the optimization variables. This approach optimizes the overall energy consumption of the collaborative robot, resulting in a reduction in energy consumption. Experimental results demonstrate that the optimized robot lowers energy consumption, and the optimal combination of time and speed reduces energy consumption by approximately 4% and 2%, respectively, compared to pre-optimization levels. These experiments validate the feasibility of the method and provide valuable insights for reducing robot energy consumption. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Modeling, Simulation & Scientific Computing. 2026/04, Vol. 17, Issue 2, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2026
- ISSN:17939623
- DOI:10.1142/S1793962326500029
- Accession Number:193365039
- Copyright Statement:Copyright of International Journal of Modeling, Simulation & Scientific Computing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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