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.

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.

Record linkage is considered a fundamental process for ensuring data quality and reliability, with critical applications in domains such as healthcare, finance, and commerce. A machine learning-based approach for optimizing record linkage in structured datasets is presented in this paper. By integrating hybrid blocking methods (combining standard blocking and sorted neighborhood approaches) with advanced similarity measures, computational overhead is significantly reduced while high accuracy is maintained. The performance of TabNet, a deep learning model designed for tabular data, is compared with traditional deep neural networks (DNNs) in the classification phase. Experimental results on a synthetic dataset of 5,000 records demonstrate that comparable precision and recall are achieved by TabNet to DNNs while execution time is reduced by over 79%. The scalability and efficiency of the proposed method are highlighted by these findings, making it well-suited for large-scale data management tasks. Practical and computationally efficient solutions for record linkage in the era of big data are contributed to by this work.

Record linkage is a fundamental step in ensuring the quality of data by detecting duplicate records within different databases. Nevertheless, dealing with big, imbalanced databases and ensuring data confidentiality is still difficult in terms of performance and precision. This paper introduces a new Privacy-Preserving Record Linkage (PPRL) method named PPRL-Stack, which uses the Bloom filter encoding technique to hide information and a Stack Ensemble structure for classification. The proposed model consists of Support Vector Machine (SVM) as a base learner and Logistic Regression (LR) as a meta-classifier in combination with the application of Sorted Neighborhood Method (SNM) technique to bring down the time complexity to O(N log N). Experiments conducted on the Freely Extensible Biomedical Record Linkage (FEBRL) and North Carolina Voter Registration (NCVR) databases prove that the proposed PPRL-Stack can obtain nearly perfect discrimination with an F1-score of 0.9921. Particularly, our proposed architecture is more than 340 times and 40 times faster than the latest Siamese Bidirectional Long Short-Term Memory (Bi-LSTM) architecture in training and validation stages, respectively.