The development of a biologically inspired mobile robot with a visual landmark recognition system

Intelligence can only be determined by the dynamic interaction with the world. Recent studies in artificial intelligence and robotics suggest that even very simple biological creatures exhibit desirable functionality and boast far greater robustness than human-designed systems. This thesis focuses on using the biologically-inspired approaches to design an evolutionary autonomous mobile robot that is able to navigate in a poorly defined environment. The implementation of complex artificial intelligence systems can be approached by decomposing the global tasks into simpler, well-specified behaviors which are easier to design and can be tuned independently of each other. The behavior-based approach is chosen to build the control system of the robot.; The analysis and design of fuzzy control laws for steering control of this biologically-inspired mobile robot are proposed. The approach uses Lyapunov's direct method to formulate a class of control laws that guarantee the convergence of the steering error. Robot behaviors can be implemented as a set of fuzzy rules which mimic expert knowledge in specific tasks. Behaviors usually emerge from implicit knowledge of the underlying process that can be converted into a set of linguistic variables and fuzzy rules. In this thesis, fuzzy logic is applied to generate obstacle avoidance behaviors with the information extracted from ultrasonic sensors. A vision-based landmark recognition system for the robot navigation tasks is implemented as the highest layer. A novel search method, based on genetic algorithms for pattern recognition in digital images, is proposed and implemented in the developed mobile robot to generate desired behaviors.