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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.

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.

Overwinding protection devices are used to brake hoisting containers before these containers reach a limited height, thereby preventing the hoisting containers from impacting the hoisting system. However, in ultra-deep shafts (depth > 1000 m), traditional overwinding protection methods fail to protect the hoisting system, because this type of hoisting system has a greater mass, kinetic energy, and inertia than the traditional hoisting system, and also the environment of ultra-deep shafts is more complex. This paper presents a novel overwinding protection method that applies a linear permanent magnet eddy current brake (LPMECB) to the hoisting system in ultra-deep shafts. This paper also finds the optimum setting parameter of permanent magnets (PMs). First, an analytical model of the LPMECB is built, and the time-domain signals of the braking force are processed via fast Fourier transform, confirming the mechanism of the optimum setting parameter. Subsequently, the simulations are conducted by establishing a finite-element model of the LPMECB; the simulations prove the existence of the optimum setting parameter of PMs and demonstrate the influence of the air gap, velocity, and conductivity on this parameter. Finally, the experimental studies are carried out on a test bench of the LPMECB to validate the analytical model and the simulation results. The results show the existence of the optimum setting parameter of PMs and prove that the air gap has an effect on this parameter.

An adaptive robust controller with non-local memory hysteresis force compensation is investigated for the precision tracking control of pneumatic artificial muscle (PAM). The proposed controller presents a two-layer cascade structure, and each layer has an adaptive law part and a robust control law part. A modified operator based Prandtl-Ishlinskii (PI) model is employed in the development of the robust control algorithm with the hysteresis feedback linearization compensation. Moreover, in the robust control law part, the problem of unbounded uncertain nonlinearities introduced by the hysteresis force term is addressed by applying an on-line monitoring method. In the adaptive law part, model parameters including weights of the modified operator are updated online by the recursive least squares estimation (RLSE) method, and then the effect of the hysteresis non-local memory characteristic is further attenuated. The tracking error is guaranteed to converge to a small residual set. Comparative experimental results demonstrate the significance of the non-local memory hysteresis force compensation, then, a desired precision can be guaranteed.

An adaptive dynamic surface controller (ADSC), which comprises an online parameter estimator and a robust control law, is developed for the pneumatic servo systems driven by the proportional directional control valves. Departing from the use of time-consuming offline fitting of the orifice area to accommodate the effect of valve dead zone, this paper employs the least-square-type indirect parameter estimation algorithm with online condition monitoring to estimate the dead-zone parameters and some other important model parameters. These accurate estimates of model parameters are utilized in the development of a precise position tracking controller for the single-rod pneumatic actuator. By using the dynamic surface control technique to synthesize the robust control law, the problem of “explosion of complexity” in traditional backstepping design method is avoided. The stability of the closed-loop system is proved by the means of the Lyapunov theory. The obtained extensive comparative experimental results verify the effectiveness of the proposed valve dead-zone compensation strategy and the high-performance nature of the ADSC in practical implementation.

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.

RGBD sensors are widely used in current robot research as they have the capacities of measuring geometrical distances and providing dense point clouds. However, it is still a difficult problem to extract semantic object distances from these point clouds. To solve this problem, a novel approach based on BP neural network is proposed in this paper. As a start point, we first analyze the complexity of obtaining semantic object distances. Then, the principle of the proposed approach is introduced in detail. Besides, in this work, Tensorflow is used to implement the BP neural network based approach. To make the training/evaluation dataset, a special software tool is developed. Finally, an object with complex geometry has been selected as a test object, and the experimental results have verified the effectiveness of the approach.

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.

In this paper, a disturbance observer based backstepping controller is developed for the electro-pneumatic servo systems with single-rod cylinder. A disturbance observer that does not require the state derivatives is firstly constructed, and based on which a nonlinear control law is then synthesized by adopting the backstepping method. By utilizing the Barrier Lyapunov Function in the controller design, a prescribed tolerance of position tracking error can be guaranteed theoretically. The stability of the closed-loop system is proven by the means of Lyapunov theory. Experimental results are presented to demonstrate the effectiveness of the proposed controller.

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.

Since pneumatic muscle has many district advantages compared with electric motor and hydraulic actuator, a 6-DOF parallel manipulator actuated by pneumatic muscles is proposed in this paper. Furthermore, a detailed system model that includes the kinematics and dynamics of parallel manipulator, the pressure dynamics and output force equation of pneumatic muscle, and an equation for the mass flow through the control valve's orifice is developed. The effectiveness of the proposed model is verified by the comparison between simulation and experimental results. Thus, the proposed model can be used in our future work to develop high performance controller.

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.