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Research on the Intelligent System Architecture and Control Strategy of Mining Robot Crowds

Author

Listed:
  • Zenghua Huang

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
    Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China)

  • Shirong Ge

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Yonghua He

    (Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China)

  • Dandan Wang

    (Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China)

  • Shouxiang Zhang

    (Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China)

Abstract

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.

Suggested Citation

  • Zenghua Huang & Shirong Ge & Yonghua He & Dandan Wang & Shouxiang Zhang, 2024. "Research on the Intelligent System Architecture and Control Strategy of Mining Robot Crowds," Energies, MDPI, vol. 17(8), pages 1-27, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1834-:d:1374021
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