The document discusses using genetic algorithms to control a robot. It proposes representing the robot's behaviors as chromosomes and using a genetic algorithm to evolve effective behaviors over generations. The behaviors are evaluated based on their ability to navigate toward a target location while avoiding obstacles. Testing in different simulated environments showed that a genetic algorithm could evolve behaviors that guide the robot along near-optimal paths to reach its target.
7. Simulator Readings: sensors, position and angle S0-S7: [0, 1023] obstacle not obstacle very detected closed 1000 1000 X Y Robot’s World angle of the robot with the world : [- , ] x y 0
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9. Controller Model Genetic Algorithm evolves robot’s attitudes Sensors Position Robot’s Angle Goal Location Motor 2 Motor 1 Khepera Simulator
10. Proposed Model based on human behavior IF Obstacle detected THEN Avoid collision, forget target ELSE S traight to the target according to the target direction END