Simultaneous Localization and Mapping (SLAM) in large-scale, complex, and GPS-denied underground coal mine environments presents significant challenges. Sensors must contend with abnormal operating conditions: GPS unavailability impedes scene reconstruction and absolute geographic referencing, uneven or slippery terrain degrades wheel odometer accuracy, and long, feature-poor tunnels reduce LiDAR effectiveness. To address these issues, we propose CoalMine-LiDAR-IMU-UWB-Wheel-Odometry (CM-LIUW-Odometry), a multi-modal SLAM framework based on the Iterated Error-State Kalman Filter (IESKF). First, LiDAR-inertial odometry is tightly fused with UWB absolute positioning constraints to align the SLAM system with a global coordinate. Next, wheel odometer is integrated through tight coupling, enhanced by nonholonomic constraints (NHC) and vehicle lever arm compensation, to address performance degradation in areas beyond UWB measurement range. Finally, an adaptive motion mode switching mechanism dynamically adjusts the robot’s motion mode based on UWB measurement range and environmental degradation levels. Experimental results validate that our method achieves superior accuracy and robustness in real-world underground coal mine scenarios, outperforming state-of-the-art approaches. We open source our code of this work on Github3to benefit the robotics community.

A LiDAR-IMU fusion system utilizing adaptive scanning is developed for high-resolution deformation monitoring of underground coal mine infrastructure, such as sealed walls. The system integrates data from a LiDAR scanner and an IMU, employing a penalty function-based scanning strategy to optimize point cloud quality. Following feature extraction and state estimation, a 3D point cloud model of the sealed wall is constructed. Deformation monitoring is achieved through point cloud segmentation, registration, and error analysis across multiple time intervals. A methodology for optimizing equipment placement on walls of varying dimensions is proposed to efficiently capture deformation details. Two metrics, PATD and PARE, are introduced to evaluate system performance. Calibration experiments using standardized boards and blocks are designed to determine optimal monitoring parameters, including distance, height, and sampling frequency. Simulated deformation experiments under real-world conditions validate the system’s rationality and accuracy.

Improving motion speed and efficiency remains a critical challenge in snake robots gait control. This paper introduces the Loop gait, a novel locomotion gait designed to enhance both speed and energy efficiency of snake robots without passive wheels. Compared to Crawler gait and S-pedal gait, which are more widely used, the Loop gait has a better motion speed (1.8 times of the Crawler gait in the same parameter) and a better motion efficiency (1.6 times of the Crawler gait in the same parameter) due to its more loop body morphology. A static stability model is developed to guide parameter optimization, addressing potential instability caused by elevated center of mass of snake robots. Experiments confirm the Loop gait’s exceptional energy efficiency and propulsion, validating the static stability model’s utility in selecting parameters.

Three-dimensional electromagnetic inversion and representation of coal seam anomalies based on ground-penetrating radar (GPR) data is a highly promising approach for real-time coal seam modeling, enabling safe, precise, and efficient sensing in coal mining shearer robots. However, traditional methods face challenges in handling sparse B-scan measurements and noise-contaminated signals while maintaining computational efficiency. To overcome these challenges, we introduce CSAInvNet, a novel collaborative learning framework that integrates three innovative neural networks for robust 3-D permittivity reconstruction. The proposed method first employs a transformer-enhanced interpolation network that generates high-density B-scan volumes from sparse inputs by learning cross-channel correlations. Then, an improved mask-guided CycleGAN architecture is proposed for simultaneously performing signal denoising and realistic noise synthesis through adversarial training with physical constraints. Finally, a 3-D inverter is established for the inverse mapping from processed B-scan data volumes to anomaly distributions with geometric preservation. Extensive simulation and field experiments demonstrate superior performance with a peak signal-to-noise ratio (PSNR) of 35.9377 and intersection over union (IoU) of 0.8677, outperforming conventional methods by 22.5% and deep learning baselines by 11.2%, respectively. Both quantitative and qualitative evaluations demonstrate the robustness and effectiveness of the proposed network in reconstructing 3-D representations of complex anomalies, such as cavities and gangue inclusions, within coal seams. To the best of our knowledge, this is the first deep learning framework that achieves simultaneous resolution enhancement, noise suppression, and 3-D inversion within a unified architecture for coal seam sensing and modeling.

Making biomimetic robots move like natural animals is an interesting problem, because this topic involves not only the low level algorithm that controls the movement of robots' bodies and limbs but also the high level control strategy that deals with different kinds of situations. Based on a certain biological assumption, a self-tuning multi-phase CPG for snake robots is proposed. This method imitates the control strategy of natural snake's movement in different environments, which enables the snake robot to move more quickly and naturally. Through kinematic and dynamic analysis of snake robots, optimal control parameters are chosen for the decision strategy. Due to the intrinsic property of the multi-phase CPG, this model can change the movement patterns and control parameters autonomously according to external information. As a result, such neural control provides a powerful but simple way to self-tune adaptable behaviors in snake robots.