An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network

In the water supply network, leakage of pipes will cause water loss and increase the risk of environmental pollution. For water supply systems, identifying the leak point can improve the efficiency of pipeline leak repair. Most existing leak location methods can only locate the leak point approximately to the node or pipe section of the pipe network, but cannot locate the specific location of the pipe section. This paper presents a framework for accurate location of water supply network leakage based on ResNet. The framework is to pinpoint leaks to specific locations along the pipeline. The leakage of two kinds of pipe networks is simulated. For a pipe network containing 40 pipes, the positioning accuracy of the pipe section is 0.94, and the MSE of the specific location of the leakage point is 0.000435. For the pipe network containing 117 pipes, the positioning accuracy of the pipe section is 0.91, and the MSE of the specific location of the leakage point is 0.0009177, and the leak location ability under different sensor arrangements is analyzed. Experiments verify the robustness and applicability of the framework.

Deep Vision Servo Hand-Eye Coordination Planning Study for Sorting Robots

Within the context of large-scale symmetry, a study on deep vision servo hand-eye coordination planning for sorting robots was conducted according to the problems of low recognition-sorting accuracy and efficiency in existing sorting robots. In order to maintain the symmetry of the picking robot, a small telescopic sorting robot with RealSense depth vision servo embedded in the manipulator was developed. The workspace and posture of picking parcels were analyzed, and the coordinate transformation model of hand-eye coordination was established for the “Eye-in-hand” mode. The hand-eye coordinated sorting test shows that the average positioning accuracy of the end in the X, Y and Z directions is 3.49 mm, 2.76 mm and 3.32 mm respectively, and the average time is 19.19 s. Among them, the average time for the mechanical arm to pick up the package from the initial position is 12.02 s, the average time for intermediate identification and calculation is 3.79 s, and the average time for placing the package is 6.9 s. The time consumed by robot arm’s action accounts for 79.8% of the whole cycle. The robot structure and the hand-eye coordination strategy with RealSense depth vision servo embedded in the robot can meet picking operation requirements, and the design of a picking robot proposed in this paper can greatly improve the coordination symmetry of fruit target recognition, detection, and picking.

A Low-Cost Global Navigation Satellite System Positioning Accuracy Assessment Method for Agricultural Machinery

The high-precision positioning and navigation of agricultural machinery represent a backbone for precision agriculture, while its worldwide implementation is in rapid growth. Previous studies improved low-cost global navigation satellite system (GNSS) hardware solutions and fused GNSS data with complementary sources, but there is still no affordable and flexible framework for positioning accuracy assessment of agricultural machinery. Such a low-cost method was proposed in this study, simulating the actual movement of the agricultural machinery during agrotechnical operations. Four of the most commonly used GNSS corrections in Croatia were evaluated in two repetitions: Croatian Positioning System (CROPOS), individual base station, Satellite-based Augmentation Systems (SBASs), and an absolute positioning method using a smartphone. CROPOS and base station produced the highest mean GNSS positioning accuracy of 2.4 and 2.9 cm, respectively, but both of these corrections produced lower accuracy than declared. All evaluated corrections produced significantly different median values in two repetitions, representing inconsistency of the positioning accuracy regarding field conditions. While the proposed method allowed flexible and effective application in the field, future studies will be directed towards the reduction of the operator’s subjective impact, mainly by implementing autosteering solutions in agricultural machinery.

DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.

Piezoelectric stick-slip actuators with flexure hinge mechanisms: A review

Piezoelectric stick-slip actuators (PSSAs) are famous for ultimate working condition adaptability, simple structure, and positioning accuracy. To meet the demand of industrial application, lots of PSSAs designed with flexure hinge mechanisms (FHMs-PSSAs) have been developed to realize the requirements of translational motion, rotational motion, multi-degree-of-freedom (multi-DOF) motion. The output performance of the FHMs-PSSAs has been greatly improved, including load capacity, speed, and accuracy; moreover, some approaches to solve the problem of the backward motion are provided as well. In this work, the working principle of FHMs-PSSAs is introduced, and the excitation signals applicable to FHMs-PSSAs are summarized. Based on the current research and development status, the progress of structure design of FHMs-PSSAs is introduced in accordance with translatory FHMs-PSSAs, rotary FHMs-PSSAs, and multi-DOF FHMs-PSSAs. Additionally, the developed analysis methods and design schemes to improve the performance are introduced, including theoretical analysis methods, consistency scheme of forward and reverse performance, suppression scheme of the backward motion, and improvement scheme of positioning accuracy. The significance of this work can be regarded as a further supplement to the previous review articles on the PSSAs, which will provide a reference and guidance for the future development of FHMs-PSSAs.

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.

High-bandwidth controller design for dual-stage actuator system in hard disk drives

Large-capacity hard disk drives are important for the development of an information society. The capacities of hard disk drives depend on the positioning accuracy of magnetic heads, which read and write digital data, in disk-positioning control systems. Therefore, it is necessary to improve positioning accuracy to develop hard disk drives with large capacities. Hard disk drives employ dual-stage actuator systems to accurately control the magnetic heads. A dual-stage actuator system consists of a voice coil motor and micro-actuator. In micro-actuators, there is a trade-off between head-positioning accuracy and stroke limitation. In particular, in a conventional controller design, the micro-actuator is required to actuate such that it compensates for low-frequency vibration. To overcome this trade-off, this study proposes a high-bandwidth controller design for the micro-actuator in a dual-stage actuator system. The proposed method can reduce the required stroke of the micro-actuator by increasing the gain of the feedback controller of the voice coil motor at low frequencies. Although the voice coil motor control loop becomes unstable, the micro-actuator stabilizes the entire feedback loop at high frequencies. As a result, the control system improves the positioning accuracy compared to that achieved by conventional control methods, and the required micro-actuator stroke is reduced.

Evaluation of Real-Time Kinematic Positioning and Deformation Monitoring Using Xiaomi Mi 8 Smartphone

Modern low-cost electronic devices can achieve high precision for global navigation satellite systems (GNSSs) and related applications. Recently, the pseudo-range and carrier phase have been directly obtained from a smartphone to establish a professional-level surveying device. Although promising results have been obtained by linking to an external GNSS antenna, the real-time kinematic (RTK) positioning performance requires further improvement when using the embedded smartphone antenna. We first investigate the observation quality characteristics of the Xiaomi Mi 8 smartphone. The carrier-to-noise-density ratio of L5/E5a signals is below that of L1/E1 signals, and the cycle slip and loss of lock are severe, especially for L5/E5a signals. Therefore, we use an improved stochastic model and ambiguity-resolution strategies to improve the short-baseline RTK positioning accuracy. Experimental results show that the ambiguity fixing rate can reach approximately 90% in 3 h of observations when using the embedded antenna, while the GPS/Galileo/BDS single-frequency combination is more suitable for smartphones. On the other hand, convergence takes 10–30 min, and the RTK positioning accuracy can reach 1 and 2 cm along the horizontal and vertical directions, respectively, if ambiguity is resolved correctly. Moreover, we verify the feasibility of using a mass-produced smartphone for deformation monitoring. Results from a simulated dynamic deformation experiment indicate that a smartphone can recognise deformations as small as 2 cm.

Processing of the Results of Satellite Inland Positioning Transport using Mobile Devices and their Visualization

The issue of spatial data visualization is currently an important element in the positioning and navigation process. The constant trend in increasing the accuracy and availability of position modules affects the widespread use of the mobile devices in transport. The paper presents creation of a three-dimensional visualization model based on ground tracks recorded in NMEA (National Marine Electronics Association) and GPX (GPS Exchange Format) formats. Additionally, the study presents an analysis of the positioning accuracy including the sky obstructions presence and the instantaneous state of the satellite constellation. The significant deterioration in positioning accuracies was noted due to the presence of sky obstructions and low movement speed during data recording. The analysis of these parameters showed the dependence of the positioning accuracy with the number of visible satellites and the HDOP (Horizontal Dilution of Precision) parameter.