# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 1 Introduction
These technologies and methods have addressed technical, environmental, and operational challenges, laying the foundation for more efficient and sustainable mining. From a theoretical standpoint, the integration of AI in industrial systems relies on a convergence of computational models capable of abstraction, reasoning, and generalization. This includes not only algorithmic sophistication but also systemic adaptability to contextual and environmental variability, which is especially relevant in the mining sector. Recent developments in neuro-symbolic AI, knowledge graph integration, and meta-learning techniques have extended the functional capacity of mining-focused AI systems by enabling better interpretability, modular transfer learning, and multi-task optimization without losing operational robustness [26, 27, 28, 29]. These paradigms allow for more context-aware and resilient decision-making processes, while also enhancing the transparency of AI outputs in high-risk industrial contexts [28, 30, 31]. Their application within real-time systems, combined with edge processing infrastructure [32, 33], supports scalable deployment in geographically distributed and connectivity-constrained operations such as remote mining facilities, where delay-sensitive decisions are critical to safety and efficiency. The bibliometric analysis, based on a search in Scopus with the Boolean formula ('Neural Networks' OR 'Deep Learning' OR 'Artificial Intelligence') AND ('Mining' OR 'Mineral Processing'), identified 15 articles published between 2022 and 2025. Using VOSviewer, a co-occurrence network with 172 keywords was generated, setting a minimum threshold of \(1\) occurrence per term. Six main clusters were identified: the red cluster (sixty terms) focuses on agricultural soils and atomic absorption spectrometry, reflecting environmental concerns. The green cluster (39 terms) groups terms such as'machine learning' and 'joint angle', highlighting technical development. The blue cluster (30 terms) relates'mining industry' to 'environmental impact', highlighting ecological risks. The yellow cluster (24 terms) emphasizes regulatory compliance, while the violet and light blue clusters (4 terms each) have less impact on the network. This analysis allows us to visualize the main trends at the intersection between artificial intelligence and mining. In synthesis, artificial intelligence (AI) emerges as a key tool for transforming the mining industry towards a more sustainable and efficient operation. Its applications focus on environmental monitoring, air quality control, and agricultural land management, addressing critical challenges such as pollutant emissions reduction and land use sustainability. Advanced methods such as machine learning and neural networks lead their implementation, enabling predictive analysis and optimization of processes such as ventilation and maintenance in mines. This approach consolidates AI as a strategic pillar to minimize environmental impacts and maximize operational efficiency in mining (See Figure 1). From another perspective and based on an updated time horizon, the evolution of artificial intelligence (AI) in mining is highlighted, focusing on the transition to more advanced and sustainable technologies. An increase in the adoption of predictive algorithms and hybrid models is projected, with emphasis on air management, emissions reduction, and real-time environmental monitoring. These solutions integrate tools such as digital twins and deep learning systems, strengthening operational efficiency and control of environmental impacts. This scenario positions AI as a key catalyst to address contemporary sustainability challenges in the mining industry (See Figure 2). The main contributions of this research are the following:  
* Development of an autonomous mobile station with artificial vision and artificial intelligence capable of identifying equipment, people, and animals in critical operational areas, optimizing safety and surveillance.