# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 1 Introduction  
The mining industry faces several complex challenges that threaten its sustainability, efficiency, and capacity for innovation in an ever-changing global environment. These challenges include declining ore grades in shallow deposits, the need to mine resources at extreme depths, and social and environmental pressures arising from the impacts of mining activity [1, 2]. The integration of advanced technologies, such as artificial intelligence, Internet of Things (IoT), and automation, has been identified as a key strategy to address these constraints, improving operational efficiency and mitigating risks [3, 4]. However, technology adoption is not without barriers, such as resistance to organizational change, lack of local technical capabilities, and high initial implementation costs [5, 6]. In this context, solutions such as smart mining, combining advanced analytics and automation, and the circular economy, focused on water recovery and tailings management, have demonstrated a positive impact on productivity and sustainability [7, 8]. Similarly, strengthening the links between local suppliers and global companies, together with the implementation of inclusive regulatory policies, is essential to overcome innovation limitations and promote sustainable development in strategic regions such as Latin America [9]. This comprehensive approach, which combines technological innovation, efficient resource management, andcoherent regulatory strategies, positions the mining industry as a key player to face the challenges of the 21 st century, ensuring its competitiveness and social responsibility. The main challenges in the adoption of artificial intelligence (AI) in the mining industry focus on the lack of technical skills, cultural resistance to change, and regulatory barriers to ensure ethical and safe use. Adapting advanced algorithms to dynamic and noisy environments, such as those that characterize mining processes, also represents a significant technical challenge [10, 11]. In addition, inadequate technology infrastructure and limited interoperability between intelligent systems hinder the effective integration of AI into mining operations, while the development of legal frameworks to regulate autonomous technologies remains nascent in many regions [12, 13, 14]. On the other hand, specific challenges such as reducing greenhouse gas emissions, improving mining safety, and training personnel to work with advanced technologies remain priorities for the sustainability and efficiency of the sector [15, 16, 17]. AI applications in mining span a variety of critical processes, from drilling optimization, mineral extraction, and recovery to predictive maintenance and risk management in coal mining operations [10, 18]. Intelligent systems have proven effective in advanced flotation monitoring, aerial inspection using drones, and failure prediction in critical machinery [11, 19, 20]. Likewise, AI technologies have been applied to reduce environmental impact through energy optimization of heavy machinery and structural damage monitoring of industrial equipment [15, 21]. More recently, tools such as text mining have been used to analyze technology trends and foster strategic planning in specific regions, including Indonesia and South Africa [22, 23]. Predominant methodologies in the use of AI in mining include supervised and unsupervised learning algorithms, hybrid models, and advanced neural network techniques. Expert systems and ConvLSTM models have been especially effective in predicting critical conditions and monitoring processes in real time [11, 19]. In addition, data mining algorithms, combined with thematic analysis and natural language processing (NLP) techniques, have facilitated the analysis of large volumes of data, such as technology trends and industry behavioral patterns [24, 25]. Other notable approaches include integrated cyber-physical systems for mining automation and advanced simulators for personnel training [14, 17].