Industrial progress of robotic automation in mining applications: A survey

Robotic autonomous systems (RASs) offer transformative potential for mining by enhancing safety and productivity, but the absence of comprehensive real-world implementation data hinders adoption. This study bridges that gap through a systematic review of deployed mining robots — including drilling rigs, haul trucks and earthmoving equipment — providing practitioners with crucial insights into operational successes and challenges. We complement this with a rigorous evaluation framework for assessing robotic autonomous mining systems (RAMSs) across component and system levels, enabling reliable predeployment testing of safety, performance and cost-effectiveness. By connecting academic research with industrial needs, the current work delivers practical tools for engineers to validate designs and gives operators actionable data to guide automation investments. The resulting synthesis not only advances RAS development but also accelerates the mining industry’s transition to safer, more efficient and sustainable operations through informed technology deployment. This dual focus on real-world applications and standardized assessment methodologies creates a vital link between technological innovation and practical implementation in mining environments. Background The mining industry is vital for supplying raw materials that fuel economic growth and technological progress, yet it faces significant challenges such as hazardous environments, unreliable communication in remote areas, and safety risks from human-operated machinery. RASs offer a promising solution by enhancing safety, efficiency and resilience in mining operations. Recent surveys highlight advancements in RAS applications, including autonomous earthmoving equipment; computer vision and machine learning for automation; subterranean exploration robots; and haulage systems. These systems integrate sensing, perception, planning and execution layers to function in unstructured mining environments. To ensure successful deployment, performance evaluation is critical during the design phase, involving hardware tests (sensors, communication and mobility) and software assessments (algorithm stability, accuracy, efficiency). However, standardized metrics remain task-dependent, with testing methods varying across studies. This review consolidates industrial and academic progress in RASs for mining, emphasizing real-world applicability, performance evaluation frameworks and future research directions to bridge gaps between theoretical development and practical implementation. Fig. 1 Taxonomy of RAMSs. Definition and taxonomy of RAMSs An RAMS operates independently or with limited human input using sensors, processors and algorithms to perceive and respond to its environment. Its four-layer architecture includes: (1) sensing: data collection, (2) perception: environmental modeling via algorithmic interpretation, (3) planning: optimal path calculation and (4) execution: motion control and error correction. As shown in Fig. 1, RAMS implementations fall into three categories based on human involvement: function-assist (partial intervention), teleoperation (remote control) and fully autonomous systems [1]. Industrial development of RAMSs RAMSs are widely applied in mining operations, implementing theoretical concepts for reliable automation. As shown in Fig. 2, key applications include autonomous drilling rigs, impact hammers, earthmoving equipment (excavators, bulldozers, wheel loaders) and haul trucks. Figure 2: Fig. 2 Major mobile robotic platforms of RAMSs. Drilling rigs, used for creating boreholes in mining operations, have evolved from 1990s prototypes to artificial intelligence (AI)-enhanced systems capable of teleoperation and full autonomy. While mobile and permanent rigs exist, autonomous drills are now commercially deployed. Impact hammers (rock breakers) lag in development; though remote operation is possible, commercialization requires further testing. Earthmoving equipment (excavators, bulldozers, wheel loaders) has seen extensive research due to cross-industry applications (mining, construction, agriculture). While many research studies show theoretical advances outpace practical implementations, with current systems reaching teleoperation or semiautonomy, this survey focuses on the latest developments by original equipment manufacturers (OEM) such as Caterpillar and Komatsu. Autonomous haulage systems (AHSs) represent mining’s most advanced RAMSs, achieving full autonomy and multirobot coordination for material transport. Their operational maturity exceeds other autonomous mining equipment. Evaluation of RAMSs This study consolidates key evaluation criteria for RAMS software systems at both component and system levels, providing practical assessment guidelines. At the component level, three core algorithms require evaluation: perception, planning and control. While individual component metrics ensure localized performance, system-level evaluation remains crucial to identify integration issues. For comprehensive assessment, we propose three system-wide metrics: (1) stability, (2) efficiency and effectiveness and (3) learnability. Future discussions The mining industry stands at a technological crossroads, where emerging innovations like Industry 5.0 [2] and large language models (LLMs) present transformative potential alongside significant challenges. AI has advanced perception and planning in RAMSs, yet achieving reliable full autonomy remains elusive. LLMs show promise for operational condition assessment and data annotation through multimodal mining data integration. Communication technologies, while improved through 5G and internet of things (IoT), still lack mining-specific standards, with current systems limited by latency and bandwidth, though impending 6G networks promise real-time coordination for autonomous fleets. Multimachine cooperation is evolving through advanced algorithms and 6G-enabled data sharing, enabling collaborative operations in harsh environments. Concurrently, mining electrification [3] is revolutionizing sustainability, with electric vehicles reducing ventilation costs by 40 percent and emissions by 30 percent in pioneering projects like Canada’s Onaping Depth. Industry initiatives like the EU’s Sustainable Intelligent Mining System and hydrogen-electric prototypes demonstrate rapid progress, showcased at events like MINExpo International 2024, where Caterpillar’s Dynamic Energy Transfer and Sandvik’s specialized mining batteries were featured. The convergence of electrification and autonomy is driving smart mine development, where AI-optimized, zero-emission equipment will enhance safety, efficiency and environmental sustainability. These interconnected advancements — in AI, communication architectures, swarm robotics and clean energy — collectively pave the way for fully autonomous, sustainable mining operations that maximize productivity while minimizing ecological impact. Conclusion This survey classifies mining RAS architectures by control modes and autonomy levels, focusing on industrial applications rather than technical details. It evaluates current robotic mining platforms and establishes performance assessment criteria for both components and complete systems. While highlighting recent progress, the study also identifies critical future development directions. Selected references Owens R, Roos C, Rosenthal S, Hill B (2022) Adapting openpit mine design fundamentals to leverage the advantages of autonomous haulage systems, MINEXCHANGE 2022 SME Annual Conference & Expo, Salt Lake City, UT, pp 1–7 Wang X et al. (2023) Smart decentralized autonomous organizations and operations for smart societies: Human–autonomous organizations for industry 5.0 and society 5.0. IEEE Intell Syst 38(6):70–74 Bao H, Knights P, Kizil M, Nehring M (2023) Electrification alternatives for open pit mine haulage. Mining 3(1):1–25

Industrial progress of robotic automation in mining applications: A survey

Authored by: Haiping Du, Linping Chan, Jun Tong, Raad Raad, Fazel Naghdy, Qinghua Guo, Yanguang Yu, Md Rabiul Islam, Faisel Tubbal, Montserrat Ros, Zheng Li and Christian Ritz

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