In order to reduce the natural frequency of the piezoelectric vibration energy harvester, improve performance of the piezoelectric vibration energy harvester, and meet the requirements of energy acquisition in the low-frequency vibration environment, a variable-section circular piezoelectric vibration energy harvester is presented. The dynamic model and electromechanical coupling model of variable-section circular piezoelectric vibration energy harvester are established. The main factors affecting the output performance of piezoelectric vibration energy harvester are analyzed. The structure parameters of piezoelectric vibration energy harvester are optimized by orthogonal experiment. An experimental platform is built to test output voltage and output power of piezoelectric vibration energy harvester. The experimental results show that when the number of energy harvester is 4 and the external load is 180KΩ, the parallel output power can reach 0.213mW, which can meet the requirements of micro-power device power supply.

The precise tracking of unmanned aerial vehicle (UAV) in coal mines remains a challenging task that faces strict energy constraints. The existing approaches mainly focus on achieving better accuracy but ignore energy consumption. This article proposes an on-demand precise tracking (OPT) framework based on the fusion of the ultrawideband (UWB) and inertial measurement unit (IMU). The fusion unscented Kalman filter (fusion UKF) is devised for energy-efficient tracking with customized performance. First, OPT provides an adaptive adjustment mechanism of UWB signal transmission to trade off between accuracy and energy consumption for UAV local positioning in coal mines. Second, we propose an on-demand trigger algorithm for UAV remote tracking to determine whether the data are transmitted to the ground server. Especially, the on-demand trigger condition is ameliorated due to the existence of packet drops in complicated coal mines. Finally, extensive simulations in the Gazebo Platform and field experiments utilizing P440 UWB nodes in the indoor and coal mine laboratories have been conducted to verify the feasibility. Results show that OPT is efficient and practical and balances the performance and energy consumption under appropriate parameters. Compared with the existing event-triggered extended Kalman filter (ET-EKF) scheme, the accuracy is improved by 10.3%, and the communication rate is contracted by 11.4% in the coal mine environment.

Deep learning has been wildly utilized in the remaining useful life (RUL) prediction of rolling bearings. Extracting valuable features effectively is a challenging task in this field. In this paper, we propose a novel competitive temporal convolutional network (CTCN) to predict the RUL of rolling bearings. Firstly, a novel dual competitive attention (DCA) is proposed to enhance the feature extraction of the deep learning model. In DCA, a unique global competition (GC) module is designed to gather the global large-pooled features as the benchmark of attention calculation, and a new multidimensional competition (MC) module is proposed to fuse the attention in two dimensions. Then, a competitive temporal convolutional block (CTCB) based on the DCA and temporal convolutional network (TCN) is designed to extract high-level features from the input sequences. Finally, a CTCN is constructed to map the original vibration signals to the RUL. The experimental results illustrate that the proposed CTCN is effective, and DCA can significantly improve the accuracy of RUL prediction. The ablation experiments show that the proposed GC and MC can improve the performance of the proposed attention. They reduce the root mean square error on average by 0.0095/8.76% and 0.0031/2.95%, the mean absolute error on average by 0.0074/ 8.46% and 0.0033/3.82%.

The idler roller is the bearing device of the belt conveyor, whose health status is crucial for the efficient transportation of materials in coal mines and other industries. In this paper, we propose a new embedded belt conveyor idler monitoring node with the capacities for self-powering, multiparameter sensing, and wireless communication for the real-time health monitoring of the idler roller. First, the architecture of the belt conveyor idler monitoring network was proposed, and three functional modules were designed for the node: an energy-harvesting module based on the Halbach permanent magnet array, an energy-management module that adapts the energy harvester to the node’s power consumption, and a control/sensing module that employs wireless communication. Subsequently, the performance of the node was tested and analyzed. The power of the energy harvester designed here was 171.33 mW, which sufficiently provides for the node’s power consumption. The energy management circuit can provide the network with a remaining operating time of 74 min. The communication test showed the influence of narrow space on the reliable communication distance of the nodes. Concluding the research, an overall test of the node was conducted, demonstrating that the node can accurately collect and transmit temperature and vibration data pertaining to the idler, meeting the monitoring requirements. The proposed node provides a technical solution for the health monitoring of belt conveyor idlers and the overall system.

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the existing state-of-the-art methods. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 3rd in the leaderboard, showing its effectiveness.

In low-light environments, the scarcity of visual information makes feature extraction and matching challenging for traditional visual simultaneous localization and mapping (SLAM) systems. Changes in ambient lighting can also reduce the accuracy and recall of loop closure detection. Most existing image enhancement methods tend to introduce noise, artifacts, and color distortions when enhancing images. To address these issues, we propose an innovative low-light visual-inertial (LL-VI) SLAM system, named LL-VI SLAM, which integrates an image enhancement network into the front end of the SLAM system. This system consists of a learning-based low-light enhancement network and an improved visual-inertial odometry. Our low-light enhancement network, composed of a Retinex-based enhancer and a U-Net-based denoiser, enhances image brightness while mitigating the adverse effects of noise and artifacts. Additionally, we incorporate a robust Inertial Measurement Unit initialization process at the front end of the system to accurately estimate gyroscope biases and improve rotational estimation accuracy. Experimental results demonstrate that LL-VI SLAM outperforms existing methods on three datasets, namely LOLv1, ETH3D, and TUM VI, as well as in real-world scenarios. Our approach achieves a peak signal-to-noise ratio of 22.08 dB. Moreover, on the TUM VI dataset, our system reduces localization error by 22.05% compared to ORB-SLAM3, proving the accuracy and robustness of the proposed method in low-light environments.