Integrated Positioning System of Unmanned Automatic Vehicle in Coal Mines

Cui, Yuming; Liu, Songyong; Jian, Yao; Gu, Congcong

doi:10.1109/tim.2021.3083903

Cited by 20 publications

(15 citation statements)

References 31 publications

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“…A HYBRID ADAPTIVE NAVIGATION FILTER We rely on the well established, model-based EKF, yet instead of a model-based adaptive approach, a data-driven approach is used to regress the momentary process noise covariance, and, as a result, creates a hybrid adaptive EKF. To this end, the navigation filter equations ( 8)-( 12) are used, but instead of using a constant process noise (7) or a modelbased adaptive process noise (18), we derive a data-driven approach to estimate the time-varying continuous process noise covariance matrix Q c . Our proposed hybrid adaptive EKF framework, suitable for any aided INS scenario, is presented in Figure 1.…”

Section: B Model Based-adaptive Noise Covariancementioning

confidence: 99%

“…The IMU signals are inserted both into the INS/GNSS filter (8) − ( 12) and the DNN model. The regressed continuous process noise covariance is plugged into (7) to obtain the discrete one, which in turn is substituted into (9). if (mod(t, ∆τ )=0) then Calculate DNN model and predict Q c k+1 .…”

Section: Hybrid Learning Approachmentioning

confidence: 99%

“…A UTONOMOUS vehicles, such as autonomous underwater vehicles (AUV) or quadrotors, are commonly equipped with an inertial navigation system (INS) and other sensors [1] to provide real-time information about their position, velocity, and orientation [2]- [7]. The INS has two types of inertial sensors; namely, gyroscopes and accelerometers.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model and Learning-Based Adaptive Navigation Filter

Klein

2022

IEEE Trans. Instrum. Meas.

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The fusion between an inertial navigation system and global navigation satellite systems is regularly used in many platforms such as drones, land vehicles, and marine vessels. The fusion is commonly carried out in a model-based extended Kalman filter framework. One of the critical parameters of the filter is the process noise covariance. It is responsible for the real-time solution accuracy, as it considers both vehicle dynamics uncertainty and the inertial sensors quality. In most situations the process noise is covariance assumed to be constant. Yet, due to vehicle dynamics and sensor measurement variations throughout the trajectory, the process noise covariance is subject to change. To cope with such situations, several adaptive modelbased Kalman filters were suggested in the literature. In this paper, we propose a hybrid model and learning-based adaptive navigation filter. We rely on the model-based Kalman filter and design a deep neural network model to tune the momentary system noise covariance matrix, based only on the inertial sensor readings. Once the process noise covariance is learned, it is plugged into the well-established, model-based Kalman filter. After deriving the proposed hybrid framework, field experiment results using a quadrotor are presented and a comparison to model-based adaptive approaches is given. We show that the proposed method obtained an improvement of 25% in the position error. Furthermore, the proposed hybrid learning method can be used in any navigation filter and also in any relevant estimation problem.

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Section: B Model Based-adaptive Noise Covariancementioning

confidence: 99%

Section: Hybrid Learning Approachmentioning

confidence: 99%

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A Hybrid Model and Learning-Based Adaptive Navigation Filter

Klein

2022

IEEE Trans. Instrum. Meas.

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“…It has the advantages of a fast transmission speed and a low power consumption. 15,16 However, in the coal mine working face, due to the high equipment density, the reflection, refraction, and diffraction of the metal surface on the pulse signal are serious, resulting in a low refresh rate and increased power consumption, which affects the range and accuracy of ultra-wide-band sensing. 17,18 The inertial navigation method is a completely autonomous sensing system.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the initialization time is long when the inertial navigation system is started. 20,21 Due to the fluctuation of coal seams in the working face, the changeable geological environment, the influence of water mist, and electromagnetic interference in the workplace, a variety of perception means cannot be applied. At present, there are still many difficulties in the accurate perception of the mining environment of the coal mine working face.…”

Section: Introductionmentioning

confidence: 99%

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

Zhao

Guo

et al. 2022

ACS Omega

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In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.

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Height information aided 3D real‐time large‐scale underground user positioning

Tang

Zhang

et al. 2022

IET Radar Sonar & Navi

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Due to the cost of inertial navigation and visual navigation equipment and lake of satellite navigation signals, they cannot be used in large-scale underground mining environment. To solve this problem, this study proposes a large-scale underground 3D real-time positioning method with seam height assistance. This method uses the ultra wide band positioning base station as the core and is combined with seam height information to build a factor graph confidence transfer model to realise 3D positioning. The simulation results show that the proposed real-time method is superior to the existing algorithms in positioning accuracy and can meet the needs of large-scale underground users.

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Integrated Positioning System of Unmanned Automatic Vehicle in Coal Mines

Cited by 20 publications

References 31 publications

A Hybrid Model and Learning-Based Adaptive Navigation Filter

A Hybrid Model and Learning-Based Adaptive Navigation Filter

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

Height information aided 3D real‐time large‐scale underground user positioning

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