Abstract
Intelligent control techniques for robotic systems have been used with some success in a wide variety of applications. In this paper, we construct a method for the intelligent control system of a robot using the fuzzy behavior-based control, which decomposes the control system into several elemental behaviors, and each one is realized by fuzzy reasoning. In particular, a module learning method is investigated for obtaining each representative group behavior, so that the robot can, consequently, acquire more general knowledge or fuzzy reasoning, than a central learning method. The proposed method is applied for an obstacle-avoidance problem of a mobile robot; the effectiveness of the method is illustrated through some simulations.
| Original language | English |
|---|---|
| Pages (from-to) | 233-243 |
| Number of pages | 11 |
| Journal | Mathematics and Computers in Simulation |
| Volume | 51 |
| Issue number | 3-4 |
| DOIs | |
| Publication status | Published - Jan 2000 |
| Externally published | Yes |
Keywords
- Behavior-based control
- Fuzzy set theory
- Genetic algorithm
- Mobile robot
- Module learning
- Subsumption architecture
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
- Numerical Analysis
- Modelling and Simulation
- Applied Mathematics