Deep Learning and AI Approaches for Autonomous Mobile Robot Navigation: A Simulation-Based Study with Real-World Deployment Perspectives
Keywords:
Artificial Intelligence, Autonomous mobile robots, Deep Learning, Real-World Robotics, Reinforcement learning, Robot navigation, Sensor fusion, SLAMAbstract
Autonomous mobile robot navigation is a core research area in artificial intelligence and robotics, enabling robots to operate effectively in complex and dynamic real-world environments. Traditional navigation approaches based on geometric modeling and rule-based planning often fail to generalize in unstructured and uncertain scenarios. In contrast, recent advances in deep learning and reinforcement learning have significantly improved perception, decision-making, and control capabilities in autonomous systems. This study proposes a hybrid AI-based navigation framework that integrates convolutional neural networks (CNNs) for perception, Simultaneous Localization and Mapping (SLAM) for state estimation, and Proximal Policy Optimization (PPO)-based deep reinforcement learning for motion planning and control. The system also incorporates sensor fusion to enhance robustness under noisy and dynamic conditions. Experimental results obtained in a ROS-Gazebo simulation environment demonstrate a navigation success rate of 94%, with substantial reductions in collision rate and execution time compared to classical methods. The findings confirm that hybrid AI architectures significantly enhance adaptability, robustness, and real-time performance in autonomous navigation tasks, although challenges such as sim-to-real transfer, safety assurance, and data efficiency remain open research problems.
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