Shaoze You

Underground gas control is a critical link in coal mine safety production, and drilling robots serve as the core equipment for gas extraction drilling operations. However, the autonomous locomotion technology of coal mine drilling robots has long been constrained by the unstructured underground environment and the limitations of existing navigation schemes, which restrict their intelligent application. To address this bottleneck, this paper conducts systematic research on key autonomous navigation technologies for coal mine drilling robots operating in narrow underground working faces, focusing on their practical operational requirements. First, a hardware scheme complying with coal mine safety standards is selected, the hardware structure and sensor layout are optimized via digital modeling, and the software interface and data interface format of the navigation system are designed. Second, an innovative 3D point cloud-based offline obstacle avoidance algorithm is proposed, which integrates a terrain analysis module, a local path planning method with maximum arrival probability, a Bézier curve-based trajectory library generation strategy, and a trajectory index construction method. Finally, simulation experiments, ground-simulated roadway field tests, and underground coal mine field experiments are performed to validate the proposed system. Experimental results demonstrate that the constructed autonomous navigation system enables smooth and safe autonomous locomotion and fixed-point parking of drilling robots, with an average parking error lower than 0.17 m, and can effectively avoid obstacles in complex environments. This research provides crucial technical support for the intelligent advancement of coal mine drilling robots.

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces.

The cooperative formation navigation of multiple unmanned ground vehicles (UGVs) is critical for enhancing the efficiency and safety of intelligent transportation and industrial internet of things systems. However, existing methods remain limited in unknown and complex environments due to insufficient adaptability, a lack of effective path recovery mechanisms, and inflexible formation adjustment, which may lead to motion failure. To address these challenges, this paper proposes an adaptive cooperative formation navigation method based on dynamic tree structure (ACFN-DTree), aiming to achieve feasible path exploration, dynamic decision recovery, and formation self-adaptation. First, a dynamic tree is constructed in obstacle-free regions based on real-time perception, preserving critical environmental information while identifying feasible directions. Then, sub-goals are selected via a spatial cost function and backtracking mechanism, guiding UGVs toward the global goal or returning to feasible branches when deadlocks occur. Furthermore, the formation compression coefficient is optimized based on the effective formation width, allowing for adaptive formation configuration adjustments. Extensive numerical and virtual simulation experiments demonstrate that ACFN-DTree exhibits significant advantages in terms of path length, formation maintenance, and path smoothness level. These results verify the effectiveness of the proposed method in enabling multiple UGVs to achieve safe navigation and adaptive formation adjustment in unknown and complex environments.

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios.

Neural Radiance Fields (NeRF) have significantly advanced photorealistic novel view synthesis. Recently, 3D Gaussian Splatting has emerged as a promising technique with faster training and rendering speeds. However, both methods rely heavily on clear images and precise camera poses, limiting performance under motion blur. To address this, we introduce Event-Informed 3D Deblur Reconstruction with Gaussian Splatting(EiGS), a novel approach leveraging event camera data to enhance 3D Gaussian Splatting, improving sharpness and clarity in scenes affected by motion blur. Our method employs an Adaptive Deviation Estimator to learn Gaussian center shifts as the inverse of complex camera jitter, enabling simulation of motion blur during training. A motion consistency loss ensures global coherence in Gaussian displacements, while Blurriness and Event Integration Losses guide the model toward precise 3D representations. Extensive experiments demonstrate superior sharpness and real-time rendering capabilities compared to existing methods, with ablation studies validating the effectiveness of our components in robust, high-quality reconstruction for complex static scenes.

In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.

Robotic mining equipment plays an increasingly important role in the coal mining industry. Due to the complexity of the confined underground environment, available localization methods are limited, and restrict the development of coal mine robots (CMRs). Ultra-wideband (UWB) is a promising positioning sensor with high ranging accuracy. However, current applications about UWB positioning in coal mine focus mainly on position information, but rarely on orientation information. Positioning accuracy is often plagued by the loss of transmitted signals and multipath effects. In this paper, a pseudo-GPS positioning system in underground coal mine, composed by noisy UWB range measurements, is proposed to provide localization service for CMRs. An Error-State Kalman Filter (ESKF) is used for fusing measurements from the inertial measurement unit (IMU) and the established UWB positioning system. Then the complete six degree of freedom (6-DOF) state estimation can be realized. Meanwhile the biases of the IMU and the translation parameters of IMU w.r.t. UWB mobile node are also estimated online to adapt to long-term operation in harsh underground environments. In addition, an UWB anchor optimal deployment strategy is discussed to deploy UWB nodes appropriately in the laneway, and maintain realistic positioning accuracy for CMR in the meantime. A large number of field tests in different environments including the actual underground coal mine were conducted. The experimental results showed that our method could obtain the pose estimation performance close to the state-of-the-art lidar odometry approach that has been currently utilized in underground coal mine, providing robust and precise localization estimation for CMR applications.

Computer vision has received a significant attention in recent year, which is one of the important parts for robots to obtain information about the external environment. Visual trackers can provide the necessary physical and environmental parameters for the mobile robot, and their performance is related to the actual application of the robot. This study provides a comprehensive survey on visual trackers. Following a brief introduction, we first analyzed the basic framework and difficulties of visual trackers. Then the structure of generative and discriminative methods is introduced, and summarized the feature descriptors, modeling methods, and learning methods which be used in tracker. Later we reviewed and evaluated the state-of-the-art progress on discriminative trackers from three directions: correlation filter, deep learning and convolutional features. Finally, we analyzed the research direction of visual tracker used in mobile robot, as well as outlined the future trends for visual tracker on mobile robot.

Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.