Knowledge base tuning using genetic algorithm for fuzzy behavior-based autonomous mobile robot
The attractive research in the field of robotics as a main alternative to conventional robot in recent years is Behavior-based mobile robot. This control architecture should generate perfect behavior action and able to handle conflicting actions that are seemingly irreconcilable, those are known as...
保存先:
| 主要な著者: | , |
|---|---|
| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2005
|
| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/1841/1/andi05_knowledgr_base_tuning.pdf http://eprints.utm.my/id/eprint/1841/ |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | The attractive research in the field of robotics as a main alternative to conventional robot in recent years is Behavior-based mobile robot. This control architecture should generate perfect behavior action and able to handle conflicting actions that are seemingly irreconcilable, those are known as Behaviour Design Problem and Action Selection Problem. This paper presents a new schema to overcome behavior-based problems based on Fuzzy Logic Controller (FLC) where the fuzzy knowledge bases are tuned automatically by Genetic Algorithm (GAs), known as Genetic Fuzzy System (GFS). The behaviors are controlled by GFS to generate individual command action. Later, a Context- Dependent Blending (DBD) based on meta fuzzy rules coordinates the commands to produce final control action. The scheme is validated using parameters of MagellanPro mobile robot and tested by simulation using MATLAB/ SIMULINK. Simulation results show that the proposed model offers hopeful advantages and has improved performance. |
|---|