What question did this study set out to answer?

To comprehensively review and categorize path planning and obstacle avoidance techniques in mobile robot navigation, spanning classical methods, modern machine learning, and emerging large language models.

August 21, 2025Journal of King Saud University - Computer and Information Sciences22 citationsOpen Access

A survey on autonomous navigation for mobile robots: From traditional techniques to deep learning and large language models

AWAbderrahim Waga SBSaid Benhlima ABAli Bekri

Key Points

To comprehensively review and categorize path planning and obstacle avoidance techniques in mobile robot navigation, spanning classical methods, modern machine learning, and emerging large language models.
Reviewed traditional graph-based algorithms (A*, Dijkstra) and geometric approaches (Voronoi diagrams, cell decomposition).
Evaluated metaheuristic strategies (GA, PSO, ACO), hybrid reinforcement learning and neural network frameworks, and the integration of large language models for natural language interaction.
Classified trade-offs across paradigms regarding computational efficiency, scalability, and environmental adaptability.
Highlighted hybrid architectures combining classical planners with deep reinforcement learning as effective solutions for avoiding local minima and enabling real-time navigation.
Identified large language models as a critical emerging interface for translating complex natural language commands into robot actions and multi-agent coordination.

Abstract

Autonomous navigation is a cornerstone of modern robotic systems. This review provides a comprehensive analysis of the landscape of obstacle avoidance and path planning techniques for mobile robots. We categorize and evaluate a range of approaches, beginning with traditional graph-based methods such as A* and Dijkstra, and geometric techniques like Voronoi diagrams and cell decomposition. The review extends to modern metaheuristic algorithms, including genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). Furthermore, we explore hybrid models that integrate traditional methods with machine learning, such as reinforcement learning (RL) and neural networks (NN). These hybrid approaches aim to address specific challenges, including escaping local minima and enabling real-time decision-making in uncertain environments. A significant focus is placed on the emerging role of Large Language Models (LLMs), analyzing their application in translating natural language commands into navigational actions and improving human-robot interaction. This work critically analyzes the trade-offs of each paradigm—including computational efficiency, scalability, and adaptability across these diverse methods. Finally, this review outlines emerging trends and open challenges, highlighting potential research directions in collaborative robotics, multi-agent systems, and the broader field of mobile robot navigation.

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