INTELLIGENT AI-BASED NAVIGATION SYSTEM FOR MOBILE ROBOTS
Keywords:
Keywords: AI Navigation, Mobile Robots, Simulation, CoppeliaSim, Reinforcement Learning, Sensor Fusion, Path PlanningAbstract
Abstract: The integration of artificial intelligence (AI) into mobile robotic
systems is redefining the landscape of autonomous navigation, decision-making, and
task execution. Traditional rule-based systems, while effective in structured
environments, often fail in dynamic or unknown terrains. This research investigates the
application of AI techniques – namely fuzzy logic and reinforcement learning – in
developing an intelligent navigation system for mobile robots within a virtual
simulation environment. Using the CoppeliaSim Edu platform, a three-wheeled
differential-drive mobile robot was modeled, equipped with various sensors (infrared,
ultrasonic, compass), and placed in a randomized maze-like environment. The robot’s
goal was to autonomously navigate the environment, avoid obstacles, and reach
predefined targets efficiently. Initially, a fuzzy rule-based controller was implemented
to provide baseline navigation logic. Subsequently, a Q-learning reinforcement
learning agent was introduced to optimize path planning through experience-based
learning. The combination of these techniques led to a marked improvement in
navigation performance, as evidenced by reduced completion times and increased
obstacle avoidance efficiency. Graphical data and tabular analysis support the
effectiveness of AI-based control versus traditional logic. The research confirms that
virtual environments, coupled with intelligent algorithms, can serve as powerful tools
for developing and testing advanced robotic control systems before deployment in
physical hardware. This approach is cost-effective, safe, and scalable for educational,
research, and industrial applications.
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