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In this paper, based on behavior-based artificial intelligence we have built a fully autonomous mobile robot. Several modules are developed for the mobile robot to implement different levels of competences and behaviors, where each module itself generates behaviors. New modules can be easily added to the robot system to improve in the competence without changing any existing modules. A vision-based landmark recognition system for robot navigation is developed as the highest layer in the subsumption architecture. A genetic-algorithm-based search method for pattern recognition of digital images is proposed and implemented to recognize artificial landmarks by searching all the predefined patterns. The vision layer is capable of generating the desired behaviors corresponding to various landmarks. A combination of eight ultrasonic sensors is designed to implement obstacle-avoidance behaviors through a set of fuzzy rules. The effectiveness of this behavior-based mobile robot is demonstrated by experimental studies.