High-precision Positioning System Research Articles

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

Method for determining seabed penetration depth in marine seismic exploration

The paper introduces a straightforward method aimed at determining the depth of penetration into the seabed during marine seismic exploration through bottom sounding. This technique was developed to support the establishment of technical specifications for the components of an underwater robotic complex intended for seismic surveys beneath ice formations. These components include a suite of autonomous underwater vehicles (AUVs) outfitted with either geophones or short streamers equipped with hydrophone sensors, alongside high-precision positioning systems. The complex comprises an underwater docking station responsible for deploying AUVs to the designated work area, managing their operations, and towing low-frequency sound emitters. Moreover, it encompasses coastal infrastructure dedicated to the maintenance of AUVs and the support of the docking station. The developed method considers various factors including the pressure exerted by the sound emitter and the energy dissipation of the probing signal due to wave front expansion, signal transmission into and back from the ground, spatial damping during signal propagation in water and soil, and reflection from oil or gas-containing lenses. Illustrative examples demonstrate the computation of ground penetration depth in shallow and deep waters, contingent upon the pressure exerted by the sound emitter towed at a depth of 100 meters. It also assesses the method’s reliability by comparing the computed outcomes against existing experimental data.

Position Estimation Method for Unmanned Tracked Vehicles Based on a Steering Dynamics Model

A position estimation method for unmanned tracked vehicles based on a steering dynamics model was developed during this study. This method can be used to estimate the position of a tracked vehicle in real time without relying on a high-precision positioning system. First, the relationship between the shear displacement of the track relative to the ground and the speed and yaw rate of the tracked vehicle during the steering process was analyzed. Next, the steering force of the tracked vehicle was calculated by using the shear force–displacement theory, and a steering dynamics model considering the acceleration of the vehicle was established. The experimental results show that this steering dynamics model produced more accurate position estimations for an unmanned tracked vehicle than did the kinematics model. This method can serve as a reference for the positioning of unmanned tracked vehicles working in special environments that cannot use precise positioning systems.

A Novel Vision- and Radar-Based Line Tracking Assistance System for Drone Transmission Line Inspection

This paper introduces a position controller for drone transmission line inspection (TLI) utilizing the integral sliding mode control (SMC) method. The controller, leveraging GNSS and visual deviation data, exhibits high accuracy and robust anti-interference capabilities. A deviation correction strategy is proposed to capture high-voltage transmission line information more robustly and accurately. Lateral position deviation is calculated using microwave radar data, attitude angle data, and deviation pixels derived from transmission line recognition via MobileNetV3. This approach enables accurate and stable tracking of transmission lines in diverse and complex environments. The proposed inspection scheme is validated in settings with 10-kilovolt and 110-kilovolt transmission lines using a drone with a diagonal wheelbase of 0.275 m. The experimental process is available in the YouTube link provided. The validation results affirm the effectiveness and feasibility of the proposed scheme. Notably, the absence of a high-precision positioning system in the validation platform highlights the scheme’s versatility, indicating applicability to various outdoor visual-based tracking scenarios using drones.

Polytopic LPV modeling and gain scheduling [formula omitted] control of ball screw with position- and load-dependent variable dynamics

Ball screw drive (BSD) is a precision transmission mechanism widely used in high-precision positioning or tracking systems. The dynamic behavior of BSD varies with position and load, which causes tracking errors and poor robustness. Therefore, this paper proposes a polytopic linear parameter varying (LPV) model to express the varying dynamic behavior of BSD. The parameters of the LPV model are identified by the closed-loop frequency domain method and Levenberg–Marquardt iterative algorithm. Based on the polytopic LPV model, an LPV gain scheduling (GS) H∞ controller is proposed for the BSD with varying dynamics. Specifically, the controller is designed through polytope-based GS representation and mixed sensitivity synthesis. The most significant part is the proposal of a GS H∞ control algorithm to implement controller parameters that change with changing dynamics. Moreover, the stability of the closed-loop system is achieved by quadratic stabilization with state feedback. Finally, identification experiments and trajectory-tracking comparative experiments are carried out. The experimental results demonstrate that the proposed polytopic LPV modeling and GS H∞ control synthesis are effective in achieving accurate trajectory tracking and excellent robustness.

Design and Analysis of a Long-Stroke and High-Precision Positioning System for Scanning Beam Interference Lithography

A macro–micro dual-drive positioning system was developed for Scanning Beam Interference Lithography (SBIL) which uses a dual-frequency laser interferometer as the position reference and exhibits the characteristics of long travel, heavy load, and high accuracy. The macro-motion system adopts a friction-driven structure and a feedforward PID control algorithm, and the stroke can reach 1800 mm. The micro-motion system adopts a flexible hinge–plus-PZT driving method and a PID control algorithm based on neural networks, which achieves sufficient positioning accuracy of this system at the nanometer level. An optical-path-sealing system was used to reduce the measurement noise of the dual-frequency laser interferometer. The static stability of the positioning system, the stepping capacity of the macro-motion system, the stepping capacity of the micro-motion system, and the positioning accuracy of the system were tested and analyzed. Additionally, the sources and effects of errors during the motion process were assessed in detail. Finally, the experimental results show that the workbench can locate at the nanoscale within the full range of travel, which can satisfy the SBIL exposure requirement.

ANALYSIS OF EXTERIOR ORIENTATION PARAMETERS ON MONOPLOTTING PROCEDURE FOR AVOIDING OBSTACLES IN UAV REAL-TIME ROUTING

Abstract. Unmanned Aerial Vehicles (UAVs) are adopted in different applications, such as mapping, logistics, and surveillance. For some applications, it is important to identify people or objects to be tracked in the image and define their coordinates in the object space. It can be done by using a photogrammetric process called Monoplotting. Moreover, by applying the Propagation of Variance and Covariance, the standard deviation from planimetric coordinates can be defined as an Exterior Orientation Parameter (EOP) function. This work evaluates the planimetric accuracy achieved using points defined in UAV images and the impact of EOPs precision. Two UAV images, the Digitals Surface Model (DSM) from the corresponding areas, the EOPs (X0, Y0, Z0, ω, φ, κ), and the platform/sensors characteristics were used for the experiments. Results showed that planimetric precision is low when using UAVs with a low-cost positioning system, especially if the points of interest in the image are far from the nadir. For UAVs with a high-precision positioning system, Inertial Measurement Unit (IMU) system is the one with a higher influence on the accuracy of planimetric coordinates, especially for oblique photographs (essential for UAV routing when avoiding obstacles). The impact of the IMU system precision increases in oblique photographs, this alerts to attention for UAV flight real-time routing guided by obstacles mapping using images.

Synthesis of parallel flexure stages with decoupled actuators using sum, intersection, and difference of screw systems

Parallel flexure systems (PFS) are used in high-precision positioning systems for a wide range of scientific, medical, and industrial applications. The controllability of the flexure system is optimized when actuators are decoupled and the output of any actuator does not affect the output of the others. In this paper, Screw Theory and Linear Algebra are used to perform the synthesis of parallel flexure systems. New procedures to compute the sum, intersection, and difference of screw systems are presented. These three operations are used to formulate the synthesis of PFS with decoupled actuators completely in terms of the freedom screw systems in combination with a graph representation of the mechanism and the implementation of constraint systems using Blanding’s rules. The methodology is illustrated with the design and redesign of three case studies: (i) a 2-DOF platform with cylindrical motion, (ii) three 3-DOF tip-tilt-piston platforms, and (iii) a 3-DOF platform with planar motion. The kinematic and static analyses for the solutions are performed analytically and then validated using finite element analyses. The designed PFS have very simple structures, high precision, and controllability.

Performances evaluation and characterization of a novel design of capacitive sensors for in-flight oil-level monitoring aboard helicopters

Capacitive sensing is one of the most used sensing techniques for a large variety of applications. In particular, Capacitive Level Sensors (CLSs) are well suited for avionics, as they provide continuous measurements over the full range of the sensor, high reliability and low maintenance costs. However, industry standard CLSs used nowadays on helicopters show low sensitivity and poor dynamical characteristics. Hence, novel geometries were investigated and the simulation results (already presented in a previous manuscript) were encouraging. Therefore, three prototypes were built and tested. This manuscript is aimed at their experimental validation obtained using a mechatronic testbench with a high-precision positioning system purposely designed and built, able to move the probes in and out of an oil tank. The used oil had well-known electrical characteristics and many different tests were carried out. With the obtained data of capacity vs. oil level, an in-depth analysis of the probes' static (sensitivity, non-linearity, hysteresis) and dynamic (rise time, settling time, final error) behaviors were performed. From the analysis results it can be clearly seen that the novel design featuring a helicoidal slit along the external electrode of the cylindrical probe provides increased sensitivity, improved first-order response and negligible hysteresis phenomenon and non-linearity error.

A Comparative Study of Factor Graph Optimization-Based and Extended Kalman Filter-Based PPP-B2b/INS Integrated Navigation

Recently, factor graph optimization (FGO)-based GNSS/INS integrated navigation has garnered widespread attention for its ability to provide more robust positioning performance in challenging environments like urban canyons, compared to traditional extended Kalman filter (EKF)-based methods. In existing GNSS/INS integrated navigation methods based on FGO, the primary approach involves combining single point positioning (SPP) or real-time kinematic (RTK) with INS by constructing factors between consecutive epochs to resist outliers and achieve robust positioning. However, the potential of a high-precision positioning system based on the FGO algorithm, combining INS and PPP-B2b and that does not rely on reference stations and network connections, has not been fully explored. In this study, we developed a loosely coupled PPP-B2b/INS model based on the EKF and FGO algorithms. Experiments in different urban road and overpass scenarios were conducted to investigate the positioning performance of the two different integration navigation algorithms using different degrades of inertial measurement units (IMUs). The results indicate that the FGO algorithm outperforms the EKF algorithm in terms of positioning with the combination of GNSS and different degrades of IMUs under various conditions. Compared to the EKF method, the application of the FGO algorithm leads to improvements in the positioning accuracy of approximately 15.8%~45.9% and 19%~41.3% in horizontal and vertical directions, respectively, for different experimental conditions. In scenarios with long and frequent signal obstructions, the advantages of the FGO algorithm become more evident, especially in the horizontal direction. An obvious improvement in positioning results is observed when the tactical-grade IMU is used instead of the microelectron-mechanical system (MEMS) IMU in the GNSS/INS combination, which is more evident for the FGO algorithm than for the EKF algorithm.

Optical characterization of high performance mirrors based on cavity ringdown time measurements with 6 degrees of freedom mirror positioning.

An instrument capable of measuring optical losses, transmission, and the radius of curvature of high reflectivity mirrors is presented. The measurement setup consists of two remote controlled hexapod systems with 6 degrees of freedom placed inside a vacuum enclosure. Mirror loss measurements are performed via the cavity ring-down time method using a linear resonant two-mirror Fabry-Perot cavity configuration. The use of high-precision positioning systems enables cavity loss mapping by transversely scanning the position of the cavity end mirror. Mirror surfaces of up to 30mm in diameter can be scanned, and the cavity length can be tuned by 120mm.

High-Performance Flux Tracking Controller for Reluctance Actuator

To meet the ever-increasing demand for next-generation lithography machines, the actuator plays an important role in the achievement of high acceleration of the wafer stage. However, the voice coil motor, which is widely used in high-precision positioning systems, is reaching its physical limits. To tackle this problem, a novel way to design the actuator using the magnetoresistance effect is argued due to the high force densities. However, the strong nonlinearity limits its application in the nan-positioning system. In particular, the hysteresis is coupled with eddy effects and displacement, which lead to a rate-dependent and displacement-dependent hysteresis effect in the reluctance actuator. In this paper, a Hammerstein structure is used to model the rate-dependent reluctance actuator. At the same time, the displacement-dependent of the model is regarded as the interference with the system. Additionally, a control strategy combining inverse model compensation and the disturbance observer-based discrete sliding mode control was proposed, which can effectively suppress the hysteresis effect. It is worthy pointing out that the nonlinear system is transformed into a linear system with inversion bias and disturbance by the inverse model compensation. What is more, the sliding mode controller based on the disturbance observer is designed to deal with the unmodeled dynamics, displacement disturbances, and model identification errors in linear systems. Thus, the tracking performance and robustness to external disturbances of the system are improved. The simulation results show that it is superior to the PI controller combined with an inverse compensator and even to the discrete sliding mode controller connected with inverse compensator, confirming the effectiveness of the novel control method in alleviating hysteresis.

Kinematic Precise Point Positioning Performance-Based Cost-Effective Robot Localization System

The use of high-precision positioning systems in modern navigation applications is crucial since location data is one of the most important pieces of information in Industry 4.0, especially for robots operating outdoors. In the modernization process of global navigation satellite system (GNSS) positioning, precise point positioning (PPP) has demonstrated its effectiveness in comparison to traditional differential positioning methods over the past thirty years. However, various challenges hinder the integration of PPP techniques into Internet of Things (IoT) systems for robot localization, with accuracy being a primary concern. This accuracy is impacted by factors such as satellite availability and signal disruptions in outdoor environments, resulting in less precise determination of satellite observations. Effectively addressing various GNSS errors is crucial when collecting PPP observations. The paper investigates the trade-off between kinematic PPP accuracy and cost effectiveness, through the examination of various influencing factors, including the choice of GNSS system (single or mixed), observation type (single or dual frequency), and satellite geometry. This research investigates kinematic PPP accuracy variation on a 10.4 km observed track based on different factors, using the GNSS system (single or mixed), and observation type (single or dual frequency). It can be concluded that mixed (GPS/GLONASS) dual frequency offers a 3D position accuracy of 9 cm, while mixed single frequency offers a 3D position accuracy of 13 cm. In industry, the results enable manufacturers to select suitable robot localization solutions according to the outdoor working environment (number of available satellites), economical constraint (single or dual frequency), and 3D position accuracy.

Indoor High Precision Positioning System Based on Visible Light Communication and Location Fingerprinting

Indoor positioning system based on visible light communication and location fingerprinting can achieve cm-level positioning accuracy, which has become an important candidate for high precision positioning. However, in the case of non-uniform and low-density location fingerprinting distribution, it will still lead to large errors and poor robustness. To solve this problem, an accurate visible light positioning (VLP) method based on location fingerprinting and meta-heuristic is proposed, and all possible cases of uneven distribution of location fingerprinting are considered by introducing the random selection rate. The effectiveness of the proposed VLP method is compared with other six VLP methods based on location fingerprinting. Simulation results show that when the signal-to-noise ratio is 20 dB, the average positioning error of the proposed VLP method is 0.51 cm, which is at least 80.23% lower than the other six VLP methods. Experimental results show that when the random selection rate is 50%, the average positioning error of the proposed VLP method is 3.11 cm, which is at least 41.87% lower than the other six VLP methods. The proposed VLP method can be applied to positioning scenarios with different location fingerprinting distributions. The research results can provide a new research idea for the fingerprinting weight calculation method.

Parameter Identification of Model for Piezoelectric Actuators.

Piezoelectric actuators are widely used in high-precision positioning systems. The nonlinear characteristics of piezoelectric actuators, such as multi-valued mapping and frequency-dependent hysteresis, severely limit the advancement of the positioning system's accuracy. Therefore, a particle swarm genetic hybrid parameter identification method is proposed by combining the directivity of the particle swarm optimization algorithm and the genetic random characteristics of the genetic algorithm. Thus, the global search and optimization abilities of the parameter identification approach are improved, and the problems, including the genetic algorithm's poor local search capability and the particle swarm optimization algorithm's ease of falling into local optimal solutions, are resolved. The nonlinear hysteretic model of piezoelectric actuators is established based on the hybrid parameter identification algorithm proposed in this paper. The output of the model of the piezoelectric actuator is in accordance with the real output obtained from the experiments, and the root mean square error is only 0.029423 μm. The experimental and simulation results show that the model of piezoelectric actuators established by the proposed identification method can describe the multi-valued mapping and frequency-dependent nonlinear hysteresis characteristics of piezoelectric actuators.

A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal

The signal measured by the maglev gyro sensor is sensitive to the influence of the instantaneous disturbance torque caused by the instantaneous strong wind or the ground vibration, which reduced the north-seeking accuracy of the instrument. To address this issue, we proposed a novel method combining the heuristic segmentation algorithm (HSA) and the two-sample Kolmogorov-Smirnov (KS) test (named HSA-KS method) to process the gyro signals and improve the north-seeking accuracy of the gyro. There were two key steps in the HSA-KS method: (i) all the potential change points were automatically and accurately detected by HSA, and (ii) the jumps in the signal caused by the instantaneous disturbance torque were quickly located and eliminated by the two-sample KS test. The effectiveness of our method was verified through a field experiment on a high-precision global positioning system (GPS) baseline at the 5th sub-tunnel of the Qinling water conveyance tunnel of the Hanjiang-to-Weihe River Diversion Project in Shaanxi Province, China. Our results from the autocorrelograms indicated that the jumps in the gyro signals can be automatically and accurately eliminated by the HSA-KS method. After processing, the absolute difference between the gyro and high-precision GPS north azimuths was enhanced by 53.5%, which was superior to the optimized wavelet transform and the optimized Hilbert-Huang transform.

Adaptive Extended Kalman Filter-Based Fusion Approach for High-Precision UAV Positioning in Extremely Confined Environments

For unmanned aerial vehicle (UAV)-based smart inspection in extremely confined environments, it is impossible for precise UAV positioning with global positioning system, owing to the satellite signal block. Therefore, the ultrawideband (UWB)-based technology has attracted extensive attention under such circumstances. However, due to the unpredictable propagation condition and the time-varying operational environment, the localization performance oscillation caused by the changing measurement noise may lead to the instability of UAV. To mitigate the effects, in this article, a high-precision UAV positioning system which integrates the inertial measurement unit and UWB with the adaptive extended Kalman filter (EKF) is proposed. Compared with the traditional EKF-based approach, the estimated and recorded information from previous processes is exploited to adaptively estimate and further control the estimation of the noise covariance matrices for the performance improvement. Finally, simulations and experiments have been conducted in extremely confined environments. According to the results, the proposed algorithm can significantly improve the position update rate, the median positioning error, the 95 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{th}}$</tex-math></inline-formula> percentile positioning error, and the average standard deviation into 88 Hz, 0.102 m, 0.192 m, and 0.052 m, which is applicable for applications in focused environments.

Communications and High-Precision Positioning (CHP2): Hardware Architecture, Implementation, and Validation.

Spectral congestion and modern consumer applications motivate radio technologies that efficiently cooperate with nearby users and provide several services simultaneously. We designed and implemented a joint positioning-communications system that simultaneously enables network communications, timing synchronization, and localization to a variety of airborne and ground-based platforms. This Communications and High-Precision Positioning (CHP2) system simultaneously performs communications and precise ranging (<10 cm) with a narrow band waveform (10 MHz) at a carrier frequency of 915 MHz (US ISM) or 783 MHz (EU Licensed). The ranging capability may be extended to estimate the relative position and orientation by leveraging the spatial diversity of the multiple-input, multiple-output (MIMO) platforms. CHP2 also digitally synchronizes distributed platforms with sub-nanosecond precision without support from external systems (GNSS, GPS, etc.). This performance is enabled by leveraging precise time-of-arrival (ToA) estimation techniques, a network synchronization algorithm, and the intrinsic cooperation in the joint processing chain that executes these tasks simultaneously. In this manuscript, we describe the CHP2 system architecture, hardware implementation, and in-lab and over-the-air experimental validation.

Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties

The magnetic shape memory alloy based actuator (MSMA-BA) is an indispensable component mechanism for high-precision positioning systems as it possesses the advantages of high precision, low energy consumption, and large stroke. However, hysteresis is an intrinsic property of MSMA material, which seriously affects the positioning accuracy of MSMA-BA. In this study, we propose a multi meta-model approach incorporating the nonlinear auto-regressive moving average with exogenous inputs (NARMAX) and Bouc–Wen (BW) models to describe the complex dynamic hysteresis of MSMA-BA. In particular, the BW model is introduced into the NARMAX model as an exogenous variable function, and a wavelet neural network (WNN) is adopted to construct the nonlinear function of the multi meta-model. In addition, iterative learning control is combined with a WNN to improve its convergence speed. A two-valued function is employed in the controller design process, so as to make use of history iteration information in updating control input. The main contribution of this study is the convergence analysis of the proposed iteration learning controller with iteration-dependent uncertainties (non-strict repetition of the initial state and varying iteration length). The experiments conducted on the MSMA-BA illustrate the validity of the proposed control scheme.

Swin-T-NFC CRFs: An encoder–decoder neural model for high-precision UAV positioning via point cloud super resolution and image semantic segmentation

3D point cloud and remote sensing images have been the primary data types for the development of high-precision positioning systems. Equipped with a LiDAR and a camera, an Unmanned Aerial Vehicle (UAV) can explore an uncharted territory and gather both 3D scans and aerial images in real time to dynamically inspect the surroundings. However, the high cost of a high-resolution LiDAR hinders the development of the perception module of an UAV. Also, it is essential to adopt accurate image semantic segmentation (SemSeg) algorithms to better understand the sensing environment. As hardware advancement is ongoing, support from the software side is crucial. A promising strategy for cost control in building a LiDAR-based positioning system is through point cloud super-resolution (SupRes), a technique that improves the point cloud resolution via algorithms. This study investigates a deep learning-based framework that adopts a classic encoder–decoder structure for both point cloud SupRes and image SemSeg. Unlike prior studies that mainly use convolutional neural networks (CNNs) for feature extraction, our model, named Swin-T-NFC CRFs, consists of a Vision Transformer (ViT)-based encoder and a fully connected conditional random fields (FC-CRFs)-based decoder, connected via a pyramid pooling module and multiple skip connections. Moreover, both encoder and decoder are coupled with a shifted window strategy that allows cross-window connection. As such, patches from different windows of the feature map can participate in self-attention computation, leading to more powerful modeling ability. Experimental results demonstrate that our method can effectively boost the prediction accuracy, reduce the error, and consistently outperform the state-of-the-art methods on simulated/real-world point cloud datasets and the urban drone dataset version 6.