Abstract
Adapting real mobile robots to complex or dynamic environments is just one of the many challenges robotics researchers face. The difficulty in such environments is in developing a simple, quick adaptive controller that adapts robots to patterns in these environments, especially when individual patterns require unique behavior from the robot. Although most standard evolutionary algorithms attempt to obtain optimal networks for such environments, this is difficult to attain due to network confusion in adapting and readapting patterns. We propose a simple adaptive controller able to learn and remember. It simplifies environments into simple groups of patterns, each of which the robot can independently learn and memorize. The memory introduced in the controller enhances the robot's ability to track its own experience and to cope with upcoming events. Experimental results show that the controller handles general complexity and gives the robot more adaptability, stability, and autonomy.
Original language | English |
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Pages (from-to) | 312-319 |
Number of pages | 8 |
Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
Volume | 13 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2009 |
Externally published | Yes |
Keywords
- Adaptive controller
- Learning and memory
- Pattern association network controller
- Symmetrical neural network
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence