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.

Autonomous mobile robots have revolutionized navigation by operating without human intervention, leveraging advanced data acquisition systems such as cameras, radar, and LIDAR, along with sophisticated planning, localization, and control algorithms. A critical challenge in this domain is path planning: determining optimal trajectories to ensure safe and efficient travel in diverse environments. This paper addresses the need to systematically evaluate trajectory-planning algorithms, whose selection directly impacts navigation performance. We present a comprehensive analysis of various classes of path-planning methods, detailing their advantages, limitations, and application contexts. By comparing these algorithms, we identify key criteria for selecting the most suitable approach for a given scenario based on environmental complexity, computational constraints, and robot configuration. Our results synthesize existing solutions into a coherent framework that guides practitioners and researchers in algorithm choice. Finally, we discuss current challenges, such as real-time adaptability and scalability, and outline future prospects to advance mobile robot navigation systems. This work offers actionable insights to improve the reliability and performance of autonomous navigation.

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.