Review of Hybrid Path Planning Techniques for Mobile Robots: Integration Between AI Techniques and Traditional Methods in Known Environments

Abstract

Mobile robots require effective and secure path planning, especially in complex and dynamic environments. Traditional algorithms such as A*, Dijkstra, and Rapidly-exploring Random Trees (RRT) offer dependable and mathematical solutions but face challenges regarding scalability, adaptability, and processing requirements in real-time applications. On the other hand, artificial intelligence (AI) techniques, such as reinforcement learning (RL) and neural networks (NNs) provide flexibility and quick decision-making but face challenges such as data dependency, optimal solution, and computational overhead. This review analyzes hybrid path planning methodologies that integrate traditional algorithms with AI techniques, utilizing the advantages of both to overcome their limitations. Hybrid approaches improve scalability, collision avoidance, and re-planning efficiency by integrating the accuracy and reliability of traditional techniques with the adaptability and learning capabilities of AI. This review categorizes and analyzes research to identify significant gaps and suggests future paths for enhancing hybrid path planning, offering insights for the development of more robust and intelligent navigation systems for mobile robots and autonomous platforms.