Artificial Intelligence in Autonomous Mobile Robot Navigation: From Classical Approaches to Intelligent Adaptation

Autonomous navigation has gained increasing attention with the rise of self‐driving robots in applications ranging from logistics to space exploration. The robot's capabilities, context, and objectives significantly influence the design and selection of efficient navigation methods. However, traditional navigation methods, reliant on rule‐based algorithms and deterministic reasoning, struggle with adaptability and robustness in complex scenarios. Artificial Intelligence (AI) offers a solution, enhancing navigation through techniques like deep learning (DL), semantic understanding, and real‐time anomaly detection. This review explores AI's role in overcoming the limitations of classical approaches, focusing on adaptability, safety, and collaboration in navigation tasks. Analyzing diverse techniques and their integration with sensors highlights the potential of AI to enable reliable, efficient, and secure operation in real‐world environments, guiding future research in intelligent robotics.