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.

The level of intelligence across various industries, including mining, has experienced a notable advancement since the introduction of Industry 4.0. However, the societal demands for employee well-being, resource conservation, and environmental preservation continues to be unavoidable challenges. Thus, the parallel system theory based Industry 5.0 is proposed, which augments industries with human-machine collaboration, virtual-real interaction and local-global balanced resources. In this letter, we proposed the concept of Mining 5.0. Mining 5.0 aims to realize green and sustainable development goals while prioritizing a human-centric perspective. This letter begins by elucidating the prerequisites, characteristics, and construction objectives of Mining 5.0. It then introduces the “4I”, “5O” and “6S” concluded framework of “Intelligent Mining Using Parallel Intelligence” for achieving Mining 5.0. In the last part, the efforts made by Chinese mining research institutions and leading enterprises in the development of mining intelligence standards, and the practical application of parallel mining within the mining sector are outlined. This letter is a summary of recent Distributed/Decentralized Hybrid Workshop on Autonomous Mining (DHW-AM) and aims at enhancing the intelligence of future mining operations.

Advancements in 4D mmWave radar with multi-sensor fusion have significantly enhanced the robustness of autonomous driving systems. In the context of “Mining 5.0” based on parallel intelligence theory, autonomous haulage need to achieve full autonomy in open-pit mines. Current systems use 3D mmWave radar, LiDAR, and cameras but have limited automation progress. This perspective discusses the limitations of these systems and how integrating 4D mmWave radar can improve mining autonomy. This perspective results from discussions at several recent Distributed/Decentralized Hybrid Workshops on Autonomous Mining (DHW-AM) and aims at enhancing the intelligence of future mining operations.

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital–model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.