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| Content Provider | Springer Nature Link |
|---|---|
| Author | Kim, Jeong Jung Lee, Jun Woo Lee, Ju Jang |
| Copyright Year | 2009 |
| Abstract | A parameter search for a Central Pattern Generator (CPG) for biped walking is difficult because there is no methodology to set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which reduces the evaluation of fitness in a real environment. |
| Starting Page | 447 |
| Ending Page | 457 |
| Page Count | 11 |
| File Format | |
| ISSN | 15986446 |
| Journal | International Journal of Control, Automation and Systems |
| Volume Number | 7 |
| Issue Number | 3 |
| Language | English |
| Publisher | Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers |
| Publisher Date | 2009-05-30 |
| Publisher Place | Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Biped robot central pattern generator particle swarm optimization Control Robotics Mechatronics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Control and Systems Engineering Computer Science Applications |
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