A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA. (15th February 2020)
- Record Type:
- Journal Article
- Title:
- A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA. (15th February 2020)
- Main Title:
- A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA
- Authors:
- Zhang, Kaipeng
Liu, Ning
Wang, Gao - Other Names:
- Watanabe Keigo Academic Editor.
- Abstract:
- Abstract : To solve the problem that the time-consuming optimization process of Genetic Algorithm (GA) can erode the expected time-saving brought by the algorithm, time-optimal trajectory planning based on cubic spline was used, after the modification to classical fitness sharing function of NGA, a dual-threaded method utilizing elite strategy characteristic was designed which was based on Niche Genetic Algorithm (NGA) with the fitness sharing technique. The simulation results show that the proposed method can mitigate the contradiction of the long term the optimization algorithm takes but a short running time the trajectory gets, demonstrating the effectiveness of the proposed method. Besides, the improved fitness sharing technique has reduced the subjective process of determining relevant parameters and the optimized trajectory results met performance constraints of the robot joints.
- Is Part Of:
- Journal of robotics. Volume 2020(2020)
- Journal:
- Journal of robotics
- Issue:
- Volume 2020(2020)
- Issue Display:
- Volume 2020, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 2020
- Issue:
- 2020
- Issue Sort Value:
- 2020-2020-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-02-15
- Subjects:
- Robotics -- Periodicals
Robotics
Periodicals
629.892 - Journal URLs:
- https://www.hindawi.com/journals/jr/ ↗
- DOI:
- 10.1155/2020/6859589 ↗
- Languages:
- English
- ISSNs:
- 1687-9600
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 12985.xml