A Comprehensive Review of Advanced AI-Driven Obstacle Avoidance Technologies for Mobile Robots in Dynamic and Unstructured Environments

This paper explores the evolution of obstacle avoidance technologies in mobile robotics, highlighting the shift from traditional methods to advanced AI-driven approaches. Traditional techniques such as geometric modeling and vision-based systems have been fundamental but often fall short in dynamic and unstructured environments due to their limited adaptability and predictive capabilities. Recent advancements in AI, including fuzzy logic, deep reinforcement learning (DRL), and end-to-end methodologies, have significantly enhanced the navigation capabilities of mobile robots, allowing them to adapt autonomously to complex settings through simulated interactions. This review discusses the integration of perception, decision-making, and control within an intelligent agent framework, emphasizing how these technologies not only enhance the robots' learning and decision-making capacities in complex scenarios but also address challenges such as computational demands and real-time processing needs. The paper aims to provide insights into the current landscape of AI-driven obstacle avoidance and outline potential future directions for enhancing real-world applications of mobile robotics.