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Parameters optimization of central pattern generators for hexapod robot based on multi-objective genetic algorithm
- Source :
- International Journal of Advanced Robotic Systems, Vol 18 (2021)
- Publication Year :
- 2021
- Publisher :
- SAGE Publishing, 2021.
-
Abstract
- In this article, a network of central pattern generators is used for the motion planning of a hexapod robot. There are many parameters in the planning network, which determine the motion performance of the hexapod robot. On the other hand, the network is a highly nonlinear coupling network, which is difficult to obtain optimal parameters by an analytical method. Optimizing these parameters to make the robot walk well is a multi-objective optimization process. There is a certain mutual exclusion relationship among the targets. To find a well-performing network as soon as possible, a multi-objective genetic algorithm is used for the process of parameter tuning. The hexapod robot simulation model is performed in Webots, and the motion performance parameters of the robot are obtained through built-in sensors and are also considered as mean values of the optimization algorithm. The optimization algorithm is written and run with MATLAB. Finally, the optimization algorithm and simulation results are proven by an experiment.
- Subjects :
- Electronics
TK7800-8360
Electronic computers. Computer science
QA75.5-76.95
Subjects
Details
- Language :
- English
- ISSN :
- 17298814
- Volume :
- 18
- Database :
- Directory of Open Access Journals
- Journal :
- International Journal of Advanced Robotic Systems
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7d4290404c64e9d9c6aea4ed808e36f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1177/17298814211044934