Positioning Accuracy Requirements Research Articles

Movement and binding control strategy based on a new type of rebar-binding robot

Purpose The paper focuses on the issue of manual rebar-binding tasks in the construction industry, which are marked by high labor intensity, high costs and inefficient operations. The rebar-binding robots that are currently available are not fully mature. Most of them can only bind one or two nodes in one position, which leads to significant time wastage in movement. Based on a new type of rebar-binding robot, this paper aims to propose a new movement and binding control that reduces manpower and enhances efficiency. Design/methodology/approach The robot is combined with photoelectric sensors, travel switches and other sensors. It is supposed to move accurately and run in a limited area on the rebar mesh through logical judgment, speed control and position control. Machine vision is used by the robot to locate the rebar nodes and then adjusts the binding-gun position to ensure that multiple rebar nodes are bound sequentially. Findings By moving on the rebar mesh with accuracy, the robot meets the positioning accuracy requirements of the binding module, with experimental testing accuracy within 5 mm. Furthermore, its ability to bind four rebar nodes in one place results in a high efficiency and a binding effect that meets building standards. Originality/value The innovative design of the robot can adapt itself to the rebar mesh, move accurately to the target position and bind four nodes at that position, which reduces the number of movements on the mesh. Repetitive and heavy rebar-binding tasks can be efficiently completed by the robot, which saves human resources, reduces worker labor intensity and reduces construction overhead. It provides a more feasible and practical solution for using robots to bind rebar nodes.

Measurement Method of Bar Unmanned Warehouse Area Based on Binocular Vision

With the development of Industry 4.0 and the implementation of the 14th Five-Year Plan, intelligent manufacturing has become a significant trend in the steel industry, which can propel the steel industry toward a more intelligent, efficient, and sustainable direction. At present, the operation mode of unmanned warehouse area for slabs and coils has become relatively mature, while the positioning accuracy requirement of bars is getting more stringent because they are stacked in the warehouse area according to the stacking position and transferred by disk crane. Meanwhile, the traditional laser ranging and line scanning method cannot meet the demand for precise positioning of the whole bundle of bars. To deal with the problems above, this paper applies machine vision technology to the unmanned warehouse area of bars, proposing a binocular vision-based measurement method. On the one hand, a 3D reconstruction model with sub-pixel interpolation is established to improve the accuracy of 3D reconstruction in the warehouse area. On the other hand, a feature point matching algorithm based on motion trend constraint is established by means of multi-sensor data fusion, thus improving the accuracy of feature point matching. Finally, a high-precision unmanned 3D reconstruction of the bar stock area is completed.

Research on an Error Compensation Method of SINS of a Mine Monorail Crane

Underground coal mines belong to the GNSS-denied environment, and the Strapdown Inertial Navigation System (SINS) has a significant advantage in the precise positioning of equipment in this environment because of its operation without requiring interaction with external information and strong anti-interference capabilities. Nonetheless, the vibrations of the installation platform adversely affect the positioning accuracy of SINS. This article focuses on the monorail crane in coal mines as the subject of research, developing a dynamic model for the motion unit consisting of the “track + drive unit + driver’s cab”, while analyzing the relationship between track roughness conditions and the vibration excitation of this unit. Subsequently, utilizing the dynamic model, the study calculated the angular and linear vibration characteristics and formulated models to address coning error and sculling error specific to the SINS in this vibration condition. Lastly, by employing a multi-sample compensation algorithm, this article compensated for positioning errors in the SINS caused by track roughness-induced vibrations during uniform straight-line motion of the motion unit, thus achieving optimal positioning information for the monorail crane. The simulation results demonstrated that employing a four-sample compensation algorithm reduces the coning error in SINS positioning calculations by a minimum of 50% and decreases the sculling error by at least 31%, satisfying the positioning accuracy requirements for precise parking of the monorail crane during the transportation phase, while establishing the foundation for autonomous precise positioning and integrated navigation of underground track transport equipment in coal mines.

Task space control of ram poses of multipoint Servo Presses

Robotic manipulators are known for their numerous degrees of freedom with which they can manipulate objects in task space. With the help of model-based control concepts, it is possible to precisely closed-loop control their pose, thus meeting high positioning accuracy requirements. As a result, they are ideally suited for carrying out flexible activities in manufacturing processes in which freely programmable tool paths are required, such as pick and place, welding or assembly tasks. If high forces have to be applied in manufacturing processes, as is the case in bulk forming processes, robots can no longer be used due to low stiffness and moderate force provision. The development of flexible forming machines with several degrees of freedom overcomes this problem. Machine tools such as the 3D Servo Press have three ram degrees of freedom and can provide forces of up to 1600 kN, but require highly accurate position control in task space. For this reason, this paper shows how control concepts from robotics can be applied to machine tools with high stiffness, high provided forces and multiple degrees of freedom. The functionality of the control is validated using the example of upsetting tests in which off-center forces are applied to the ram and the control counteracts tilting of the ram.

Seismic behavior and simplified hysteretic model of precast concrete wall panels with bolted connections under cyclic loading

Bolted prefabricated concrete wall panel structures are considered to be of great advantage in the current construction industry as they allow efficient recycling or direct replacement of aging components for structural renewal and repair. However, the parts of bolted connectors still have many problems such as complex construction and high positioning accuracy requirements. Furthermore, comparative studies on different bolted connections have been limited to date. This paper proposes a simplified tensile-bolt connection and a shear bolt-steel plate connection for low-rise wall-based structures in earthquake-prone zones, which can adapt to the fast-easy and reliable installation manners on construction sites. Three tension-bolted specimens and three shear-bolted specimens were tested under quasi-static cyclic loading. The seismic performances studied include failure modes, hysteretic behaviors, deformation, energy dissipation capacity, stiffness degradation, and steel strain distributions. The results indicated that both types of bolted specimens meet the seismic safety of low-rise buildings. By analyzing the typical skeleton curve and hysteretic loop of the specimens, based on a linear regression fitting analysis, the characteristic points of the proposed skeleton model curves were proposed which could evaluate the restoring characteristics of the curves well. A simplified restoring force model was proposed which has a high evaluation accuracy. The research results can give references to the further application of bolt-connected fabricated wall panel structures.

Positioning Accuracy Prediction and Tolerance Allocation of Cycloid Reducers Based on a BDTCA Model

Abstract The reliability of the prediction model and test scheme for the positioning accuracy of the cycloid reducer is very important. This paper proposes an efficient bidirectional drive tooth contact analysis (BDTCA) model that can simultaneously analyze the forward transmission error (TE), the reverse TE, and the global lost motion (GLM) of a cycloid drive, considering a variety of manufacturing and assembly errors (including pin assembly error). The results of the BDTCA case studies show that the sensitivity of the pin radius error to the GLM considering pin assembly error is twice that of the sensitivity without considering pin assembly error. Therefore, the influence of pin assembly error on the GLM cannot be ignored. The equivalent error model for BDTCA is established based on Latin hypercube sampling and template measurement, and the positioning accuracy of 50,000 reducer virtual prototypes is predicted. According to the positioning accuracy requirements, the tolerance allocation is optimized by using the discrete particle swarm optimization algorithm, and the qualified rate after optimization is significantly improved. A bidirectional drive test (BDT) scheme is proposed according to the BDTCA model. By comparing with the traditional hysteresis curve test, the superiority of the BDT scheme and the rationality of the tolerance allocation optimization are proved. The sensitivity of the pin radius error to the GLM is obtained through a comparison BDT, which verifies the correctness of the BDTCA model considering pin assembly error. This BDTCA model provides more reliable theoretical guidance for the design and manufacture of cycloid reducers.

An Asymmetric Velocity Profile for Minimizing Wafer Slippage and Settling Time of a Wafer Transport Robot

This paper presents a control solution for minimizing the takt time of a wafer transfer robot that is widely used in the semiconductor industry. To achieve this goal, this work aims to minimize the transfer time while maximizing the transfer accuracy. The velocity profile is newly designed, taking into consideration parameters such as end effector deformation, changes in friction, vibrations, and required position accuracy. This work focused on the difference between the robot’s acceleration and deceleration phases and their contributions to wafer dynamics, resulting in an asymmetric robot motion profile. Mixed cubic and quintic Bezier curves were adopted, and the optimal profile was obtained through genetic algorithms. Additionally, this work combines its newly developed motion profile with an iterative learning control to ensure the best wafer transportation process time. With the presented method, it is possible to achieve a significant reduction in takt time by minimizing wafer slippage and vibration while maximizing robot motion efficiency. All development processes presented in this paper are verified through both simulation and testing.

A Study on a Novel Inverted Ultra-Short Baseline Positioning System and Phase Difference Estimation

Autonomous underwater vehicles (AUVs) are important tools for exploring and studying the ocean. To improve the stealthiness of AUV positioning, a new type of inverted ultra-short baseline (i-USBL) positioning system is proposed in this paper. The system uses a surface GPS buoy as a positioning base station to transmit positioning signals to the AUV, which receives the signals through the i-USBL and calculates its own coordinates. In this way, the power consumption of the AUV will be lower, improving its endurance. In addition, azimuth observation is one of the key observation quantities in ultra-short baseline positioning systems. Therefore, this paper derives two phase difference estimation algorithms for azimuth measurement, which are the Least Mean Square (LMS) algorithm and the Discrete Fourier Transform (DFT) algorithm. Simulation results show that when the signal-to-noise ratio (SNR) is less than or equal to 0 dB, the DFT algorithm has higher accuracy; when SNR is greater than or equal to 10 dB, LMS has slightly higher accuracy than DFT; however, DFT is more stable than LMS at all SNRs. When SNR is 20 dB, the maximum azimuth measurement error of LMS and DFT is 0.3301° and 0.2204°, respectively, which can meet the positioning accuracy requirements. The average running times of LMS and DFT are 0.085 s and 0.011 s, respectively, and LMS has a faster running speed. In summary, the LMS algorithm can be used for azimuth observation when the SNR is good, and DFT can be applied when the SNR is poor.

Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance

Visible light positioning (VLP) technology is a classic application of visible light communication (VLC), which inherits the advantages of VLC and applies it to the field of positioning. LED (light-emitting diode) is a type of light source. Because of its high brightness, aesthetically pleasing characteristics, and ease of installation, it is used in a variety of indoor lighting applications. However, most of the current VLP technology is still in the laboratory simulation stage and cannot be used in industry or life on a large scale due to various reasons, such as accuracy and cost. Because of the large size of LED flat panel lamps, there are almost no VLP applications with LED flat panel lamps as the emitting light source. Therefore, this paper proposes a VLP technology combining LED flat panel light and a barcode, with a single flat panel light at the transmitting end and a smartphone with a camera at the receiving end, to achieve fuzzy positioning. The paper further uses the angle sensor to assist in designing the “pseudo-two-light positioning” algorithm and selects 16 test points for experiments, and the average positioning error can reach a minimum of 6.5023 cm, achieving centimeter-level positioning accuracy requirements.

Formation flying along artificial halo orbit around Sun–Earth L2 point for interferometric observations

This paper proposes a method of using multiple spacecraft flying in formation in an artificially reduced halo orbit around the 2nd Lagrange point in the Sun–Earth system (SEL2) to make interferometric observations. One of the requirements for interferometric observations is to collect baseline vectors between telescopes or antennas. A shape-based approach is introduced to design a formation flight orbit that spirally spreads to satisfy this requirement. In this approach, orbits are designed on the basis of a linear theory. However, the formation orbits based on the linear theory quickly diverge because of the unstable dynamics around the SEL2. Therefore, the state-dependent Riccati equation, which allows for nonlinear control, is applied to orbit maintenance. The control system consists of two components: one for maintaining the reference orbit around the SEL2 and the other for controlling the relative position between the multiple spacecraft. The performance of the developed formation flight system is verified through a numerical simulation, confirming that the position accuracy requirement of the infrared interferometer is satisfied. It is also shown that both controls can be achieved with a low-thrust magnitude by using an electric propulsion system.

Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader

Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement.

Message Passing-Based 9-D Cooperative Localization and Navigation With Embedded Particle Flow

Cooperative localization (CL) is an important technology for innovative services such as location-aware communication networks, modern convenience, and public safety. We consider wireless networks with mobile agents that aim to localize themselves by performing pairwise measurements amongst agents and exchanging their location information. Belief propagation (BP) is a state-of-the-art Bayesian method for CL. In CL, particle-based implementations of BP often are employed that can cope with non-linear measurement models and state dynamics. However, particle-based BP algorithms are known to suffer from particle degeneracy in large and dense networks of mobile agents with high-dimensional states. This paper derives the messages of BP for CL by means of particle flow, leading to the development of a distributed particle-based message-passing algorithm which avoids particle degeneracy. Our combined particle flow-based BP approach allows the calculation of highly accurate proposal distributions for agent states with a minimal number of particles. It outperforms conventional particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we compare the proposed method to a centralized particle flow-based implementation, known as the exact Daum-Huang filter, and to sigma point BP in terms of position accuracy, runtime, and memory requirement versus the network size. We further contrast all methods to the theoretical performance limit provided by the posterior Cramér-Rao lower bound (PCRLB). Based on three different scenarios, we demonstrate the superiority of the proposed method.

3D Positioning Method for Pineapple Eyes Based on Multiangle Image Stereo-Matching

Currently, pineapple processing is a primarily manual task, with high labor costs and low operational efficiency. The ability to precisely detect and locate pineapple eyes is critical to achieving automated pineapple eye removal. In this paper, machine vision and automatic control technology are used to build a pineapple eye recognition and positioning test platform, using the YOLOv5l target detection algorithm to quickly identify pineapple eye images. A 3D localization algorithm based on multiangle image matching is used to obtain the 3D position information of pineapple eyes, and the CNC precision motion system is used to pierce the probe into each pineapple eye to verify the effect of the recognition and positioning algorithm. The recognition experimental results demonstrate that the mAP reached 98%, and the average time required to detect one pineapple eye image was 0.015 s. According to the probe test results, the average deviation between the actual center of the pineapple eye and the penetration position of the probe was 1.01 mm, the maximum was 2.17 mm, and the root mean square value was 1.09 mm, which meets the positioning accuracy requirements in actual pineapple eye-removal operations.

Feature-Enhanced CenterNet for Small Object Detection in Remote Sensing Images

Compared with anchor-based detectors, anchor-free detectors have the advantage of flexibility and a lower calculation complexity. However, in complex remote sensing scenes, the limited geometric size, weak features of objects, and widely distributed environmental elements similar to the characteristics of objects make small object detection a challenging task. To solve these issues, we propose an anchor-free detector named FE-CenterNet, which can accurately detect small objects such as vehicles in complicated remote sensing scenes. First, we designed a feature enhancement module (FEM) composed of a feature aggregation structure (FAS) and an attention generation structure (AGS). This module contributes to suppressing the interference of false alarms in the scene by mining multiscale contextual information and combining a coordinate attention mechanism, thus improving the perception of small objects. Meanwhile, to meet the high positioning accuracy requirements of small objects, we proposed a new loss function without extra calculation and time cost during the inference process. Finally, to verify the algorithm performance and provide a foundation for subsequent research, we established a dim and small vehicle dataset (DSVD) containing various objects and complex scenes. The experiment results demonstrate that the proposed method performs better than mainstream object detectors. Specifically, the average precision (AP) metric of our method is 7.2% higher than that of the original CenterNet with only a decrease of 1.3 FPS.

Operators’ Requirements for SSA Services

The space data association (SDA), an association of global satellite operators working to ensure a controlled, reliable, and efficient space environment, has run a survey among its members to gather data on their Conjunction Assessment concept of operations. These include collision avoidance Go/No-Go metrics, collision avoidance targets, and operational constraints. This paper assesses the various positional accuracy requirements of space situational awareness (SSA) data associated with each of these diverse "Go/No-Go" metrics as employed in the conjunction mitigation processes used for space traffic coordination and spaced traffic management. These metrics include miss distance at the time of closest approach (TCA), componentized miss distance (e.g., TCA radial separation to preclude collision even when in-track or cross-track separations or uncertainties are unknown), and maximum collision probability and estimated actual probability. A common practice is to approximate a spacecraft’s hardbody with an encapsulating sphere. This one-shape-fits-all approach eliminates the need to determine orientation, but results in an overestimated object volume and an overinflated probability unless both satellites are actually spheres. The dependence of collision probability on orientation and configuration/shape of the satellites at TCA is examined in contrast to the use of an encapsulating spheres to produce more representative probabilities. To overcome the lack of knowledge of the enveloping box's orientation a spectrum of collision probability values corresponding to a range of box orientations, from which the interrelationship between attitudinal knowledge and position accuracy required for a given collision probability threshold can be determined. It was found that such an approach can typically reduce probability by a factor of 3 or more. The interrelationships between SSA positional accuracy, the operator-selected Go/No-Go metric and its threshold, timeliness, and resulting maneuver frequency is also explored. For example, the necessity to perform a collision avoidance maneuver adhere to a squared relationship on the adopted miss-distance threshold. The miss distance threshold adopted by the operator should, if done properly, be a function of the estimated accuracy of the primary and secondary objects as a function of time. This paper concludes by comparing the accuracy requirements derived for each metric above with estimates of positional accuracy observed in actual SSA data fusion experiments conducted this past year. In many situations, the accuracy of legacy and commercial SSA systems is insufficient to support the adopted Go/No-Go metric without using comprehensive data fusion techniques. Recommendations for operators are provided.

Research and Implementation of Indoor 3D Positioning Algorithm Based on LED Visible Light Communication and Corresponding Parameter Estimation.

In the era of mobile Internet, the application of various positioning-based location service systems is becoming more and more common. In addition, the traditional radio positioning system is limited in the use of special environments such as mines, hospitals, and gas stations, and long-term electromagnetic radiation can cause potential damage to the human body. Compared with the traditional wireless positioning technology, VLC-based positioning technology has a good application prospect in the field of indoor wireless positioning. Compared with traditional radio positioning technology, the use of VLC technology to achieve indoor positioning is different in that the system design and layout need to consider the basic needs of indoor lighting; that is, the layout of multiple visible light sources in the room should meet the minimum illumination requirements of any area of the room. Since the layout structure of the light source that only considers the lighting requirements or only considers the positioning accuracy requirements is not the same, in the design process of the indoor visible light wireless positioning system, it is necessary to consider the overall optimization layout of multiple indoor visible light sources under the conditions of lighting and positioning constraints. This paper mainly optimizes indoor positioning from the aspects of light source layout, reflected light intensity distribution, and noise model.

Scanning strategy for surface defects evaluation of large fine optical components

Defects, such as scratches and digs etc., play an important role in quality control of fine optical components. Dark field microscopic imaging has become one of the most commonly means for surface defects detection. However, the imaging field of view is very limited compared with the increasing aperture of the test parts and sub-aperture scanning is usually employed to increase the detection aperture while ensuring the system resolution. The problems that classic “S” Shape Sub-Aperture (SSSA) strategy faces are the high positioning accuracy requirement for the translation stage and the time-consuming processing of huge number of sub-apertures. Herein, a Sub-Region and Sub-Aperture (SRSA) scanning strategy is proposed. It divides the aperture of test part into sub-regions. Within each sub-region, classic scanning strategies and stitching methods can be employed the same time when next sub-region is being scanned. The whole aperture of the test part can be obtained when all the sub-regions are stitched together. Thus the positioning accuracy requirement of translation stage is reduced from the whole aperture to a sub-region while most of the sub-aperture stitching and processing time is covered within the scanning process. The relationship between the positioning accuracy of translation stage and the sizes of sub-aperture/sub-region is discussed in detail. Comparison experiments show the superiority of the proposed SRSA scanning strategy with which large-aperture, high precision scanning detection of large-aperture optical components can be accomplished efficiently and economically.

Study on the Positioning Accuracy of the GNSS/INS System Supported by the RTK Receiver for Railway Measurements

Currently, the primary method for determining the object coordinates is positioning using Global Navigation Satellite Systems (GNSS) supported by Inertial Navigation Systems (INS). The main goal of this solution is to ensure high positioning availability, particularly when access to satellite signals is limited (in tunnels, areas with densely concentrated buildings and in forest areas). The aim of this article is to determine whether the GNSS/INS system supported by the RTK receiver is suitable for the implementation of selected geodetic and construction tasks in railway engineering, such as determining the place and extent of rail track deformations (1 cm (p = 0.95)), the process of a rapid stocktaking of existing rail tracks (3 cm (p = 0.95)) and for design and construction works (10 cm (p = 0.95)), as well as what the impact of various terrain obstacles have on the obtained positioning accuracy of the tested system. During the research, one INS was used, the Ekinox2-U by the SBG Systems, which was supported by the Real-Time Kinematic (RTK) receiver. GNSS/INS measurements were conducted on three representative sections varying in terms of terrain obstacles that limit the access to satellite signals during mobile railway measurements in Tricity (Poland). The acquired data allowed us to calculate the basic position accuracy measures that are commonly used in navigation and transport applications. On this basis, it was concluded that the Ekinox2-U system can satisfy the positioning accuracy requirements for rapid stocktaking of existing rail tracks (3 cm (p = 0.95)), as well as for design and construction works (10 cm (p = 0.95)). On the other hand, the system cannot be used to determine the place and extent of rail track deformations (1 cm (p = 0.95)).

Prediction of GPS Satellite Clock Offset Based on an Improved Particle Swarm Algorithm Optimized BP Neural Network

Satellite clock offset is an important factor affecting the accuracy of real-time precise point positioning (RT-PPP). Due to missing real-time service (RTS) products provided by the International GNSS Service (IGS) or network faults, users may not obtain effective real-time corrections, resulting in the unavailability of RT-PPP. Considering this issue, an improved back propagation (BP) neural network optimized by heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer (HPSO-BP) is proposed for clock offset prediction. The new model uses the particle swarm optimizer to optimize the initial parameters of the BP neural network, which can avoid the instability and over-fitting problems of the traditional BP neural network. IGS RTS product data is selected for the experimental analysis; the results demonstrate that the average prediction precision of the HPSO-BP model for 20-min and 60-min is better than 0.15 ns, improving by approximately 85% compared to traditional models including the linear polynomial (LP) model, the quadratic polynomial (QP) model, the gray system model (GM (1,1)), and the ARMA time series model. It indicates that the HPSO-BP model has reasonable practicability and stability in the short-term satellite clock offset prediction, and its prediction performance is superior to traditional models. Therefore, in practical applications, the clock offset products predicted by the HPSO-BP model can meet the centimeter-level positioning accuracy requirements of RT-PPP.

A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems

Automated valet parking (AVP) has attracted much attention as the entry point to autonomous driving. In an indoor environment, high-precision positioning systems are essential for AVP. Ultra-wideband (UWB) is one of the most widely adopted techniques. However, the base station placement significantly influences the system’s positioning accuracy, especially for the irregular architecture of underground parking lots. This article proposes a three-stage practical and economical layout planning approach for UWB base stations, including determining the deployment strategy and layout parameters and comprehensive adjustment and scheme verification. The approach considers regional differentiation accuracy requirements for AVP, such as ramp area, surface fluctuation area, and narrow area. The adopted positioning method of a UWB system is the time difference of arrival (TDOA), and the evaluation index of positioning accuracy is the horizontal dilution of precision (HDOP). Through experimental tests in an actual parking lot, the proposed approach is confirmed to ensure stability and economy with fewer UWB base stations and can meet the positioning accuracy requirements of AVP.