Tan Joven, Melkoumian Noune, Harvey David, Akmeliawati Rini
Discipline of Mining and Petroleum Engineering, School of Chemical Engineering, The University of Adelaide, Adelaide 5005, Australia.
School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide 5005, Australia.
Biomimetics (Basel). 2025 Mar 14;10(3):181. doi: 10.3390/biomimetics10030181.
Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It presents a systematic classification of each methodology, emphasizing their key advantages and disadvantages as well as considering real-life mining application scenarios, including hazard detection, autonomous transportation and energy-efficient drilling. Case studies are citied to demonstrate how these methodologies work together, and an extensive comparison table considering their applications at mines, such as Boliden, Diavik Diamond Mine, Olympic Dam and others, presents a summary of their scalability and practicality. This paper highlights future directions such as multi-robot coordination and hybrid NIAs, to improve operational resilience and sustainability. It also provides a broad overview of biomimicry and critically examines unresolved issues like real-time adaptation, parameter tuning and mechanical wear. The paper aims to offer a comprehensive insight into using bio-inspired models to enhance mining efficiency, safety and environmental management, while proposing a road map for resolving the issues that continue to be a hurdle for wide adaptation of these technologies in the mining industry.
环境挑战、高安全风险和运营效率低下是采矿业面临的一些问题。本文提供了一个综合观点，通过将群体机器人技术、自然启发算法（NIAs）和其他基于仿生学的技术整合到一个单一框架中来解决这些问题。它对每种方法进行了系统分类，强调了它们的主要优缺点，并考虑了实际采矿应用场景，包括危险检测、自主运输和节能钻探。文中引用了案例研究来展示这些方法如何协同工作，并且一个广泛的比较表考虑了它们在诸如博利登、戴维克钻石矿、奥林匹克坝等矿山的应用情况，总结了它们的可扩展性和实用性。本文强调了多机器人协调和混合自然启发算法等未来发展方向，以提高运营弹性和可持续性。它还对仿生学进行了广泛概述，并批判性地审视了诸如实时适应、参数调整和机械磨损等未解决的问题。本文旨在全面深入地探讨使用受生物启发的模型来提高采矿效率、安全性和环境管理水平，同时提出一个路线图，以解决那些仍然是这些技术在采矿业广泛应用的障碍的问题。
