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Under severe working conditions in mines, common wear detection methods are difficult to apply to the hoisting cage sliding sleeves. This article proposes a wear detection method based on flexible printed circuit (FPC), which is conceived for the wear detection of sliding sleeves or the edge of complex curved surfaces under severe conditions. First, a new type of sensor based on FPC is proposed, and its detection principle is presented. Then, according to the wear mechanism of sliding sleeves, a wear detection system is designed and manufactured. In order to express wear value, a formula about the relationship between the number of broken wires and wear value is established. Finally, a series of verification experiments are carried out, and the obtained results confirm the feasibility of proposed approach and explain the causes of voltage fluctuations.

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.

An innovative wind-induced energy harvester utilizing a piezoelectric bimorph with a pulling strip was designed based on Karman vortex street theory to collect wind energy from a rotating machinery for powering some devices. A mathematical model was initially established for the model force analysis, and the voltage generated by the piezoelectric bimorph were calculated. The stress distribution on the piezoelectric bimorph was also examined to prevent the piezoelectric material from cracking. A series of experiments were conducted according to the results of the numerical analysis. Both the numerical analysis and the experiments demonstrate that: first, rotation speed is the most important factor that affects the power generation capacity of the piezoelectric bimorph, and with a suitable installing angle, an output power of 1 mW can be easily acquired when the rotation speed exceeds 140 r/min; and, second, choosing an appropriate installing angle can increase the power generation capacity more effectively than rectifying the diameter of the cylindrical obstacle.

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. The 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 normally 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 building a globally 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.

Rail is an important part of coal mine hoisting system, and its fault detection is an important work of mine daily maintenance. In this paper, a new inspection robot system and corresponding detection method are proposed to detect the faults in the rail of coal mine hoisting system. The robot system will replace the manual inspection work in the mine, and will greatly improve the efficiency of inspection. The robot is driven by a magnetic wheel to ensure its smooth motion on a vertical rail. In order to increase the adsorption capacity of the magnetic wheel, the halbach array based on multi block magnets is adopted in the magnetic wheel, and the relationship between the number of magnets and the adsorption force is analyzed. In addition, aiming at the problem of rail fault detection, a new method is proposed to estimate the rail deformation by using the robot's attitude perception. A method of robot positioning on rail is also proposed by combining fault detection as landmark and mileage estimation. Finally, the proposed method is verified by experiments. This inspection robot system can improve the efficiency of rail inspection of mine hoisting system, and the proposed detection scheme can also be extended to railway rail inspection.

Robotic mining equipment is playing an increasingly important role in coal mine operations. Due to the complexity of underground occlusion environment, the localization methods available are limited, which restricts the development of coal mine robots (CMRs). Ultra Wideband (UWB) is a promising positioning sensor with accurate ranging capacity, but it needs to overcome the transient signal loss caused by multipath effect and metal block in coal mine. Range measurements from UWB can only provide 3 degrees of freedom (DOF) position without orientation, which is not enough to operation for CMRs under space-constrained coal mine. In this paper, a pseudo-GPS positioning system composed by UWB range measurements is proposed to provide the position of CMRs. Additionally, an Error State Kalman Filter (ESKF) is used for fusing measurements from Inertial Measurement Unit (IMU) and UWB positioning system. The complete 6 DOF state estimation is established, and the biases of IMU and the calibration parameters of IMU w.r.t. the UWB mobile node are also estimated online to accommodate the long-term operation in underground harsh environment. Experiments in different motion conditions show that our approach can provide robust and precise 6 DOF state estimation for CMRs.

With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

The 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 with highly challenging task in semi-structured laneway environment. In this paper, we propose a novel real-time 3D SLAM based on Normal Distributed Transform (NDT) which also take pose graph optimization and loop closure to further improve the consistency of mapping. We innovatively extract the floors and walls as plane nodes to construct constraints, in addition to applying pose nodes from the lidar odometry. Edges of the graph are constructed by the observation constraints between pose nodes and plain nodes. A simple and effective loop detection method is used via odometry-based in conjunction with appearance-based approach to build a global consistent map. The proposed method has been evaluated on the public dataset KITTI and field tests in a simulated underground coal mine. The results indicate that our algorithm can achieve lower computational complexity and lower drift in well-conditioned scenes and degenerate scenes, which can provide pose estimation and environment description for CMRRs to realize remote control assistance and automatic navigation in the coal mine rescue missions.

Based bionic principle, this paper built a multi-phase central pattern generator model capable of producing multiply bionic gaits. This model could realize the arbitrary adjusting and transforming between different movement types of snake robots by introducing external excitatory, which will be helpful to improve the environments adaptability of snake robots. In this paper, the stability of arbitrary segments multi-phase central pattern generator was proved. The influence of multi-phase central pattern generator model parameters to system outputs was analyzed. For conclude the optimization strategy of multi-phase central pattern generator outputs amplitude, the relationship between optimal amplitude of snake robots and environments parameters in serpentine locomotion. Constrains between model parameters and external excitatory were built, which enabled snake robots controlled by multi-phase central pattern generator to move in three dimensional space and adapt to environments. Finally, the validity of this bionic control method and the similarity of snake gaits were verified with the snake robot platform.

Visual method is a potential way to intuitively grasp the surface damage state of wire ropes to guarantee its reliability and safety. However, the various appearance characteristics of ropes are seriously affected by noise and effective inspection methods are lacking; therefore, efficient preprocessing and damage detection methods are urgently needed. Aiming at the influence of illumination, feature dimension, and the performance of different classification algorithms, this paper presents a novel method for the damage detection of wire ropes based on texture features, called WR-LBPML. To uniform illuminate and highlight textures of the rope images, different homomorphic filtering algorithms were compared and applied. A total of 59 texture features were extracted from rope surface images using uniform local binary patterns (u-LBP) operators. Due to the redundancy of the dataset, the features were processed using principal component analysis. Finally, we conducted a comparative study of three machine learning algorithms. Extensive experiments and comparisons of rope damage detection indicated that the proposed method can achieve optimal performance (accuracy: 93.3%, efficiency: 0.04 ms per sample) using a support vector machine with 10 features extracted by u-LBP operator, in which the images were filtered by a Gaussian low-pass filter with a block size of 1.

Because of the closed geographic conditions and the effect of uncertain factors during a catastrophe in an underground coal mine, a high failure rate of the sensor nodes after a disaster is a common phenomenon. To ensure normal communications after a disaster, an impact cushioning device is designed to ensure survival of the sensor nodes during the disasters like the tunnel collapse, gas explosion, and so on. First, a model for the spring absorbing system is built; an analysis based on the model shows that the impact cushioning device can survive the free impact due to the extremely short impact time and the small displacement despite the large force from the instantaneous impact. Second, a collision model between the impact cushioning device and the rock is built; an analysis based on the model shows that both the circuit boards and the shell of the device can survive a tunnel collapse because the maximum stress values of the shell material and the circuit boards during the collision process are much less than their yield strength values. Finally, we use a triangular wave to simulate the shock wave used to test the collision model. An analysis of the test shows that the shell of the device and circuit boards are still safe and reliable under the effects of a gas explosion for the same reason that the maximum stress values of the shell material and the circuit boards in the gas explosion process are much lower than their yield strength values.

This article proposes a novel gait on cylinders for a snake robot as well as arboreal concertina locomotion gait, including the generation method. The gait on the cylinder of a snake robot is a kind of spatial motion planning that is difficult in manual design. In this article, a planar gait of a snake robot is first constructed, followed by cylindrical gait via a transformation mechanism. In addition, a gait generating scheme of arboreal concertina locomotion gait of snake robots on cylinders is constructed and verified via a simulation platform. Such gait makes advantages on cylindrical motion capability of snake robots and the proposed generation approach for gait construction can be promoted to other cylindrical gaits, reducing design difficulty.

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.

In this paper, we propose a new CPG model for the snake-like robot being able to adapt to different environments. This model can produce typical serpentine, sidewinding and traveling wave locomotion gaits and switch between them automatically based on a simple input signal. The stability of this model is verified and the parameter restrictions are determined. Then, we investigate the relationship between the properties of CPG output and the parameters. Finally, the adaptive characters of this model is discussed. We found that there is an amazing similarity between the locomotion patterns produced by the CPG model and those of the real snake. The contribution of this work makes the snake-like robot be more adaptive to complex environments.