Abstract
The paper deals with the implementation of a behavior-based control and learning controller for autonomous inspection robots. The presented control architecture is designed to be used in the mobile robot (Amigo) for the visual inspection of ventilation systems. The main aim of the authors’ study is to propose a behavior-based controller with neural network-based coordination methods. Preliminary results are promising for further development of the proposed solution. The method has several advantages when compared with other competitive and/or co-operative approaches due to its robustness and modularity.
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