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Machines
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Key Technologies in Intelligent Mining Equipment
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Key Technologies in Intelligent Mining Equipment

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1719

Special Issue Editors

Dr. Lei Si

E-Mail Website
Guest Editor
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: pose accurate perception; autonomous navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Jianbo Dai

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
Interests: coal mine efficient intelligent mining technology and equipment Intelligent mining robot

Special Issue Information

Dear Colleagues,

In recent years, there has been a significant surge in the development and adoption of intelligent mechanical equipment across various industries. Intelligent mechanical equipment, empowered by cutting-edge technologies such as artificial intelligence, machine learning, the Internet of Things, and robotics, has revolutionized traditional manufacturing, construction, transportation, and other sectors. This special issue aims to explore the latest advances, challenges, and applications of intelligent mining equipment, providing a platform for researchers, engineers, and practitioners to share their insights and experiences.

Intelligent Control Systems: Novel control algorithms and strategies for intelligent mining equipment, including adaptive control, predictive control, and reinforcement learning-based control.

Artificial Intelligence and Machine Learning: Applications of AI and machine learning techniques in intelligent mining equipment, such as pattern recognition, fault diagnosis, and predictive maintenance.

Sensing and Perception: Advanced sensors and perception technologies for intelligent mining equipment, including vision-based systems, LiDAR, and sensor fusion techniques.

Human-Machine Interaction: Design principles and technologies for enhancing human-machine interaction in intelligent mining equipment, including augmented reality interfaces and collaborative robotics.

Autonomous and Semi-autonomous Systems: Development and deployment of autonomous and semi-autonomous systems in manufacturing, agriculture, logistics, mining and other domains.

Safety and Reliability: Methods and technologies for ensuring the safety and reliability of intelligent mining equipment, including risk assessment, fault tolerance, and safety standards compliance.

Case Studies and Applications: Real-world case studies, applications, and best practices of intelligent mining equipment in various industries, highlighting their impact on efficiency, productivity, and sustainability.

Dr. Lei Si
Dr. Jianbo Dai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent control systems of mining equipment
  • artificial intelligence and machine learning in mining sensing and perception of mining sensors
  • human-machine interaction of mining robot
  • safety and reliability of mining technologies
  • case studies and applications of intelligent mining

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Published Papers (2 papers)

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Research

19 pages, 5885 KiB  
Article
Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era
by Kui Liu, Bin Mei, Qing Li, Shuai Sun and Qingping Zhang
Machines 2024, 12(6), 419; https://doi.org/10.3390/machines12060419 - 18 Jun 2024
Viewed by 581
Abstract
Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for [...] Read more.
Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for a paradigm shift towards unmanned mining operations. With this study, we addressed the urgent need for safe and efficient production based on intelligent unmanned mining systems in open-pit mines. A collaborative production planning model was developed for an intelligent unmanned system comprising multiple excavators and mining trucks. The model is formulated to optimize multiple objectives, such as total output, equipment idle time, and transportation cost. A multi-objective optimization approach based on the genetic algorithm was employed to solve the model, ensuring a balance among conflicting objectives and identifying the best possible solutions. The computational experiments revealed that the collaborative production planning method significantly reduces equipment idle time and enhances output. Moreover, with the proposed method, by optimizing the configuration to include 6 unmanned excavators, 50 unmanned mining trucks, and 4 unloading points, a 92% reduction in excavator idle time and a 44% increase in total output were achieved. These results show the model’s potential to transform open-pit mining operations by using intelligent planning. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
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17 pages, 4689 KiB  
Article
A Walking Trajectory Tracking Control Based on Uncertainties Estimation for a Drilling Robot for Rockburst Prevention
by Jinheng Gu, Shicheng He, Jianbo Dai, Dong Wei, Haifeng Yan, Chao Tan, Zhongbin Wang and Lei Si
Machines 2024, 12(5), 298; https://doi.org/10.3390/machines12050298 - 28 Apr 2024
Viewed by 750
Abstract
A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode [...] Read more.
A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention. Full article
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
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