In recent robotics fields, much attention has been focused on utilizing reinforcement learning (RL) for designing robot controllers, since environments where the robots will be situated in should be unpredictable for human designers in advance. However there exist some difficulties. One of them is w
A framework for defining and learning fuzzy behaviors for autonomous mobile robots
✍ Scribed by Humberto Martínez Barberá; Antonio Gómez Skarmeta
- Publisher
- John Wiley and Sons
- Year
- 2002
- Tongue
- English
- Weight
- 144 KB
- Volume
- 17
- Category
- Article
- ISSN
- 0884-8173
- DOI
- 10.1002/int.1000
No coin nor oath required. For personal study only.
✦ Synopsis
In this paper we show our work on the use of fuzzy behaviors in the field of autonomous mobile robots. We address here how we use learning techniques to efficiently coordinate the conflicts between the different behaviors that compete with each other to take control of the robot. We use fuzzy rules to perform such fusion. These rules can be set using expert knowledge, but as this can be a complex task, we show how to automatically define them using genetic algorithms. We also describe the working environment, which includes a custom programming language (named BG) based on the multi-agent paradigm. Finally, some results related to simple goods-delivery tasks in an unknown environment are presented.
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