Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected.

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

Based on wireless multimedia sensor networks (WMSNs) deployed in an underground coal mine, a miner’s lamp video collaborative localization algorithm was proposed to locate miners in the scene of insufficient illumination and bifurcated structures of underground tunnels. In bifurcation area, several camera nodes are deployed along the longitudinal direction of tunnels, forming a collaborative cluster in wireless way to monitor and locate miners in underground tunnels. Cap-lamps are regarded as the feature of miners in the scene of insufficient illumination of underground tunnels, which means that miners can be identified by detecting their cap-lamps. A miner’s lamp will project mapping points on the imaging plane of collaborative cameras and the coordinates of mapping points are calculated by collaborative cameras. Then, multiple straight lines between the positions of collaborative cameras and their corresponding mapping points are established. To find the three-dimension (3D) coordinate location of the miner’s lamp a least square method is proposed to get the optimal intersection of the multiple straight lines. Tests were carried out both in a corridor and a realistic scenario of underground tunnel, which show that the proposed miner’s lamp video collaborative localization algorithm has good effectiveness, robustness and localization accuracy in real world conditions of underground tunnels.

This paper presents a vehicle autonomous localization method in local area of coal mine tunnel based on vision sensors and ultrasonic sensors. Barcode tags are deployed in pairs on both sides of the tunnel walls at certain intervals as artificial landmarks. The barcode coding is designed based on UPC-A code. The global coordinates of the upper left inner corner point of the feature frame of each barcode tag deployed in the tunnel are uniquely represented by the barcode. Two on-board vision sensors are used to recognize each pair of barcode tags on both sides of the tunnel walls. The distance between the upper left inner corner point of the feature frame of each barcode tag and the vehicle center point can be determined by using a visual distance projection model. The on-board ultrasonic sensors are used to measure the distance from the vehicle center point to the left side of the tunnel walls. Once the spatial geometric relationship between the barcode tags and the vehicle center point is established, the 3D coordinates of the vehicle center point in the tunnel’s global coordinate system can be calculated. Experiments on a straight corridor and an underground tunnel have shown that the proposed vehicle autonomous localization method is not only able to quickly recognize the barcode tags affixed to the tunnel walls, but also has relatively small average localization errors in the vehicle center point’s plane and vertical coordinates to meet autonomous unmanned vehicle positioning requirements in local area of coal mine tunnel.

Unmanned Aerial Vehicles (UAVs) can significantly improve the autonomy of the mining industry, and self-localization is the key to autonomous flights of underground UAVs. A localization method of visual-inertial sensor data fusion is proposed in this paper. The method aims to improve the localization accuracy and robustness of underground UAVs in dynamic environments. First, an algorithm for dynamic point detection and rejection is presented, which combines a semantic segmentation neural network, an optical flow method, and an epipolar constraint method. Second, a visual-inertial sensor fusion algorithm is used to enhance performance in areas lacking static visual features. It can also provide absolute scales to the localization results, as opposed to monocular systems. Finally, a hand-held multi-sensor data collection system is developed with accurate calibration, for imitating flights of underground UAVs and easing data collection in real underground tunnels. We evaluate our proposed localization method and compare it with state-of-art method VINS-Mono on both the public EuRoC dataset and our own collected data in underground tunnels. Experimental results show that the proposed visual-inertial localization method can improve the accuracy by more than 67it can be applied to underground dynamic scenes with high robustness and accuracy.