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.

The ability to navigate autonomously from the start to its final goal is the crucial key to mobile robots. To ensure complete navigation, it is mandatory to do heavy programming since this task is composed of several subtasks such as path planning, localization, and obstacle avoidance. This paper simplifies this heavy process by making the robot more intelligent. The robot will acquire the navigation policy from an expert in navigation using machine learning. We used the expert A*, which is characterized by generating an optimal trajectory. In the context of robotics, learning from demonstration (LFD) will allow robots, in general, to acquire new skills by imitating the behavior of an expert. The expert will navigate in different environments, and our robot will try to learn its navigation strategy by linking states and suitable actions taken. We find that our robot acquires the navigation policy given by A* very well. Several tests were simulated with environments of different complexity and obstacle distributions to evaluate the flexibility and efficiency of the proposed strategies. The experimental results demonstrate the reliability and effectiveness of the proposed method.

Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environments. In this paper, we propose a novel deep learning-based framework for 2D trajectory prediction in grid environments. The framework employs attention mechanisms specifically designed for path planning tasks. In particular, we design an Attention U-Net architecture that employs attention gates for effective path area focusing and residual connections for efficient feature selection. To validate our method, the Attention U-Net architecture is trained on 5000 randomly sampled 40 × 40 environments and tested on a separate test set of 200 environments. The experimental results show that the Attention U-Net architecture significantly outperforms the A* algorithm. It expands 62% fewer nodes (207.6 vs. 543.11) and achieves near-optimal path lengths (99.8% of optimal) and planning speed (0.78 ms vs. 1.19 ms). Furthermore, the Attention U-Net architecture achieves a 100% success rate for A* path planning with the attention heuristic, demonstrating the effectiveness of the attention heuristic for path planning.