MiningLLM: Towards Mining 5.0 via Large Language Models in Autonomous Driving and Smart Mining
Yuchen Li, Luxi Li, Zizhang Wu, Zhenshan Bing, Yunfeng Ai, Bin Tian, Zhe Xuanyuan, Alois Knoll, Long Chen
Abstract
Minerals serve as the cornerstone of contemporary societal infrastructure and diverse industries. However, the mining sector perennially confronts transportation challenges marked by elevated expenses, diminished efficiency, and significant environmental ramifications. Although autonomous driving has been accomplished in open scenarios, its development in specialized areas remains limited. The open-pit mine, one of the typical unstructured environments, presents several challenges, encompassing complex geological conditions, and strict requirements for intelligent algorithms. These factors create formidable barriers to the implementation of autonomous driving technology in mining areas. The utilization of Large Language Models (LLMs) provides indispensable insights for addressing these challenges. This article introduces an assistant-driven approach via LLMs to realize Mining 5.0, representing an elevated level of intelligent mining operations. This exploration delves deeply into the necessity and feasibility of this approach from various perspectives, providing substantial theoretical support for the development of mining areas.