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.

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

An accurate description of laneway space with self-localization is a key issue when coal mine rescue robots (CMRRs) perform post-disaster exploration and rescue missions. 3D Simultaneous Localization and Mapping (SLAM) is an effective but time-critical and highly challenging task in complex laneway scenarios, especially after disasters. In this paper, we propose a novel real-time 3D SLAM based on Normal Distributed Transform (NDT) that employs pose graph optimization and loop closure to further improve mapping consistency. We innovatively extract floors and walls in the laneway as plane nodes to construct landmark constraints, in addition to applying pose nodes from the lidar odometry via NDT. A lightweight and effective loop detection method is conducted using odometry with an appearance-based approach to build a global consistent map. The proposed method was evaluated on a public dataset, and field tests in an underground coal mine were performed. Results indicate that our algorithm can achieve lower computational complexity and drift, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in coal mine rescue missions.

In this letter, we propose a tightly coupled LiDAR-inertial-visual (LIV) state estimator termed LIVER, which achieves robust and accurate localization and mapping in underground environments. LIVER starts with an effective strategy for LIV synchronization. A robust initialization process that integrates LiDAR, vision, and IMU is realized. A tightly coupled, nonlinear optimization-based method achieves highly accurate LiDAR-inertial-visual odometry (LIVO) by fusing LiDAR, visual, and IMU information. We consider scenarios in underground environments that are unfriendly to LiDAR and cameras. A visual-IMU-assisted method enables the evaluation and handling of LiDAR degeneracy. A deep neural network is introduced to eliminate the impact of poor lighting conditions on images. We verify the performance of the proposed method by comparing it with the state-of-the-art methods through public datasets and real-world experiments, including underground mines. In underground mines test, tightly coupled methods without degeneracy handling lead to failure due to self-similar areas (affecting LiDAR) and poor lighting conditions (affecting vision). In these conditions, our degeneracy handling approach successfully eliminates the impact of disturbances on the system.

To improve the performance of position sensorless control with a single current sensor in low-speed range, an improved current reconstruction method is proposed. High-frequency voltage is injected to estimate the rotor position. The time interval between two sampling moments is fixed as the minimum sampling window time. Therefore, the phase current can be compensated by an online adaptive one-step method. And the time-aligned three-phase currents can be reconstructed. The influence of the error between time-aligned reconstructed currents and the full sensors sampling currents on sensorless control is discussed. With the proposed method, the injection frequency can be improved to half the switching frequency. Finally, a permanent magnet synchronous motor is sensorless controlled with the proposed method in the experiments.