During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.

Intelligent mining technology, as the core driving force for the digital transformation of the mining industry, integrates cyber-physical systems, artificial intelligence, and industrial internet technologies to establish a “cloud–edge–end” collaborative system. In this paper, the development trajectory of intelligent mining technology has been systematically reviewed, which has gone through four stages: stand-alone automation, integrated automation and informatization, digital and intelligent initial, and comprehensive intelligence. And the current development status of “cloud–edge–end” technologies has been reviewed: (i) The end layer achieves environmental state monitoring and precise control through a multi-source sensing network and intelligent equipment. (ii) The edge layer leverages 5G and edge computing to accomplish real-time data processing, 3D dynamic modeling, and safety early warning. (iii) The cloud layer realizes digital planning and intelligent decision-making, based on the industrial Internet platform. The three-layer collaboration forms a “perception–analysis–decision–execution” closed loop. Currently, there are still many challenges in the development of the technology, including the lack of a standardization system, the bottleneck of multi-source heterogeneous data fusion, the lack of a cross-process coordination of the equipment, and the shortage of interdisciplinary talents. Accordingly, this paper focuses on future development trends from four aspects, providing systematic solutions for a safe, efficient, and sustainable mining operation. Technological evolution will accelerate the formation of an intelligent ecosystem characterized by “standard-driven, data-empowered, equipment-autonomous, and human–machine collaboration”.

The truck–shovel system is the most common material transportation system in open-pit mines. The configuration of trucks and shovels directly affects the efficiency and cost of transportation in open-pit mines. Under the condition that the types and quantities of trucks and shovels are known, in order to obtain the optimal configuration scheme in the open-pit mine transportation system this paper presents a method to determine the optimal scheme by conducting experiments based on the simulation truck–shovel system model in Flexsim software. We test candidate configuration schemes that are solved by the mathematical model with daily minimum production and expected profit constraints in the simulation model, and finally obtain the optimal truck–shovel configuration scheme that meets the ore output requirements of each loading point. Through simulation experiments, the daily production of the optimal truck–shovel configuration scheme is 3.75% higher than that of the original mine scheme and the profit is increased by 3.85%. The results show that the open-pit truck–shovel system constructed by Flexsim has great research potential and value for the optimization of truck–shovel configuration schemes.

In mine operations, the safe operation of transportation equipment is crucial to ensure the safety of miners and the efficiency of mine production. However, it is notable that there is little research on perception technology for unstructured environments such as underground mining tunnels. The underground mining environment is characterized by its intricate nature, with narrow passageways, dim lighting, and complex spatial topological structures. Large-scale mining trucks operating in such environments have a restricted field of view and pose a serious safety hazard. In this paper, we propose an underground mining obstacle detection method based on 3D light detection and ranging (LiDAR) technology to augment the environmental perception capabilities of mining vehicles. This method uses point cloud data collected by LiDAR as input, employing an improved random sample consensus (RANSAC) to segment rugged ground points. Additionally, an innovative point cloud processing module for tunnel walls and the application of Euclidean clustering and obstacle recognition strategies ensure accurate obstacle detection. Experimental results demonstrate that the proposed method achieves a detection accuracy of over 95% within a 50-meter region of interest, and the running time is kept within 0.14 seconds on an ordinary computer. The effectiveness of the proposed method is discussed across varying distances, numbers, and tunnel types, revealing satisfactory outcomes and robust applicability. The proposed efficient method meets the requirements of underground mining truck obstacle detection, making a substantial contribution to underground unmanned production.

Real-time and accurate bucket pose estimation plays a vital role in improving the intelligence level of mining excavators, as the bucket is a crucial component of the excavator. Existing methods for bucket pose estimation are realized by installing multiple non-visual sensors. However, these sensors suffer from cumulative errors caused by loose connections and short service lives caused by strong vibrations. In this paper, we propose a method for bucket pose estimation based on deep neural network and registration to solve the large registration error problem caused by occlusion. Specifically, we optimize the Point Transformer network for bucket point cloud semantic segmentation, significantly improving the segmentation accuracy. We employ point cloud preprocessing and continuous frame registration to reduce the registration distance and accelerate the Fast Iterative Closest Point algorithm, enabling real-time pose estimation. By achieving precise semantic segmentation and faster registration, we effectively address the problem of intermittent pose estimation caused by occlusion. We collected our own dataset for training and testing, and the experimental results are compared with other relevant studies, validating the accuracy and effectiveness of the proposed method.

The autonomous walking of an underground load-haul-dump (LHD) machine is a current research hotspot. The route of an underground LHD machine is generally definite, and most research is based on the logic of positioning-decision control. Based on a reinforcement learning algorithm, a new autonomous walking training algorithm, Traditional Control Based DQN (TCB-DQN), combining the methods of traditional reflective navigation and reinforcement learning deep q-networks (DQN), is proposed. Compared with the logic of location-decision control, TCB-DQN does not require accurate positioning, but only determines how to reach the endpoint by sensing the distance from the endpoint. Through experimental verification, after using the TCB-DQN algorithm for training in a simple tunnel, the LHD machine could achieve a walking effect similar to that of a human driver’s manual operation, while after training in a more complex tunnel, the TCB-DQN algorithm could reach the endpoint smoothly.