Attitude Alignment Research Articles

Mapping Anti-Refugee Attitude Networks in the Classroom: Examining the Relationship Between Migrant Children’s Attitude Alignment and National Identification

ABSTRACT Irish schools have been rapidly diversifying since the early 2000s. As increased diversity often leads to increased prejudicial attitudes, children with migration backgrounds may struggle to gain acceptance and a sense of belonging as Irish. There are several strategies migrant children may take in response to prejudicial attitudes they perceive in their classroom, including adopting prejudicial attitudes toward other migrant groups – such as refugees – to align with the majority, or distancing themselves from the negative attitudes of the majority at the expense of national identification. We examine these strategies using a novel network method which identifies the positions of each child in relation to others in their classroom based on their attitude similarity. The study uses data from 54 primary school classes (N = 969 children). We find that migrant children were not identifiable by distinct patterns of anti-refugee attitudes compared to those of Irish national children. National identification was not related to the alignment of attitudes to Irish peers. Alignment of attitudes toward one’s classmates, however, was associated with higher identification with the class, for both migrant and non-migrant children. These findings highlight the class context as a relevant environment in which attitudes and identity are jointly navigated.

Hybrid compliant control with variable-stiffness wrist for assembly and grinding application

This research presents a novel robot system that combines active and passive components to enhance compliance and dependability. The system is based on a continuous variable stiffness wrist. A wrist was created that met the requirements and a combination of active and passive control methods was suggested to insert and regulate forces effectively. The control strategy is based on the Cosserat rod model, with the fundamental concept being calculating the position and orientation of the component using data on the force exerted during contact between the parts and the stiffness of the contact between the shaft and hole components. This process converts the hard assembly into a flexible contact. Compliance is monitored via force and vision sensors, which allows for the shaft-hole assembly operation to be carried out even with attitude alignment problems, resulting in a notable decrease in the precision needed for component alignment. Initially, the camera supplies the first positional data of the shaft component for the robotic system. In addition, the performance of the wrist with variable stiffness is evaluated in terms of stiffness. Additionally, the calculation of relative deformation between components is examined using contact force information. Moreover, a robust active/passive hybrid insertion control technique, which relies on contact force, is proposed. Finally, the shaft-hole assembly task substantiates the necessity for contact force monitoring in the insertion assembly process. This control technique has demonstrated its efficacy in ensuring passive-compliant assembly performance. Furthermore, the variable stiffness wrist has been employed in robotic grinding for surfaces with curved contours to validate its effectiveness.

Robust adaptive non‐linear alignment algorithm for SINS/DVL integrated navigation system based on variational Bayesian

AbstractThe problem of dynamic attitude alignment for strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation has become a research hotspot. Underwater complex environment makes DVL output vulnerable to non‐Gaussian noise pollution, which makes it difficult to converge the SINS misalignment angle to within 1° based on analytical coarse alignment. This is to say, the performance of SINS fine alignment via Kalman filter will be degraded because the model of initial alignment exhibits non‐linearity. The inaccurate and/or unknown prior information of measurement noise statistical characteristics will also degrade the filtering performance. To ensure the SINS/DVL alignment accuracy under non‐linear, non‐Gaussian and uncertain conditions, this paper proposed a robust adaptive unscented Kalman filter (UKF) based on Variational Bayesian (VB) method (VBRAUKF). The proposed VBRAUKF improves the adaptability and robustness of UKF from the following two main aspects. Firstly, an adaptive estimation strategy for the measurement noise covariance is designed based on the VB algorithm, which can restrain the effect of inaccurate observation model and improve the adaptive ability of the filtering method. Secondly, an expansion factor is designed based on Mahalanobis distance algorithm, which can restrain the effect of non‐Gaussian noise and improve the robustness of the filtering method. The experimental results for the problem of the SINS/DVL dynamic alignment under mixed Gaussian noise and/or outlier conditions demonstrate the superiority of the proposed VBRAUKF over the traditional ones.

Strategic attitude expressions as identity performance and identity creation in interaction

We assess the strategic alignment of attitudes and the active construction of attitude-based identity across two studies. Study one assessed the twitter response (hashtags in English) to the war in Ukraine for five months after Russia’s first invasion of Ukraine 2022 (N = 8149). Results demonstrated that individuals publicly expressed hashtags similar to others close to them in the followership network, showing their support for Ukraine and condemnation of the Russian invasion in qualitatively different ways. Study two was a preregistered Prolific experiment with geographical European participants ran in September, 2022 (N = 1368). Results demonstrated that attitude interaction with ingroup members motivated interactants towards attitude alignment, and attitude alignment strengthened the identification that motivated the alignment in the first place. Results suggest that attitude expression is performative and constrained by one’s group relationship with one’s audience and the definition of social identity can be constrained by opinion-based identity performance.

Research on 3D Measurement and Rapid Location Technique for Assembling the Special-Shaped Structural Segments

The pylon of the Xinshougang Bridge, a welded steel box arch with a variable cross-section, exhibits asymmetrical design and unevenly inclined limb ends. The segmented assembly of this uniquely shaped steel pylon necessitates precise splicing. In this paper, a novel three-point method for segment attitude positioning is proposed based on the local coordinate system. This method facilitates accurate coordinate translation and attitude alignment between the section preparation facility and the construction site. Furthermore, a predictive methodology for segment posture during erection is introduced, aiming to minimize misalignments at the connection interfaces. This approach accounts for potential deviations arising in manufacturing and erection phases. The synergistic application of these methods effectively ensures the precise geometric shape of the bending and twisting steel tower throughout its phased construction.

An Improved Unit Quaternion for Attitude Alignment and Inverse Kinematic Solution of the Robot Arm Wrist

This paper introduces a new method for kinematic modeling of the robot arm by deriving a new elegant mathematical formula based on the axis vector with the tangent of the rotation angle. For this purpose, an innovative analytical quaternion is introduced through integration between Axis-Invariants and unit quaternion features named Ju-Gibbs quaternion, which expresses the body rotation with non-redundant parameters compared with the quaternions in literature. Two theorems based on the new form of the quaternion are developed and proved for the kinematic modeling of the robot arm. The first is attitude alignment, which is essential in multiaxial rotation systems. The second theorem for the wrist inverse kinematic (IK) solution is utilized to obtain the joint variables for the last joints of the end effector. In order to verify the effectiveness and accuracy of the proposed method, a numerical example and simulation of different structural configurations of robot and human arms are intensively studied. The novel quaternion provides a new tool for kinematic analysis and reduces the computational complexity of the kinematic solutions of the Robot-Arms wrist. Furthermore, the method laid a new foundation for the IKs of multi-axis systems based on Axis-Invariant and tangent quaternion.

A Fast Continuous Self-Calibration Method for FOG Rotational Inertial Navigation System Based on Invariant Extended Kalman Filter

For a given device, the rapidity and accuracy of device error coefficient calibration and attitude alignment of the rotational inertial navigation system mainly depend on the convergence rate of the nonlinear filtering algorithm. The traditional extended Kalman filter method has no theoretical convergence guarantee for nonlinear systems and requires a long time in the actual calibration process. Since the attitude is subject to the 3-D rotation transformation group, the error transfer equation can be better linearized by defining the error on the lie group to use the new observation information fully. This article proposes a primary method of using the invariant extended Kalman filter to carry out an inertial navigation system. In the continuous rotation of the framework, the accelerometer, gyroscope, and framework angle are used to calibrate the error coefficient of the accelerometer and gyroscope and estimate the base alignment error angle. Simulation and experiment results show that the proposed method can accomplish alignment and calibration in 10 min under a large initial attitude error.

Micro-Vision Based High-Precision Space Assembly Approach for Trans-Scale Micro-Device: The CFTA Example

For assembly of trans-scale micro-device capsule fill tube assemblies (CFTA) for inertial confinement fusion (ICF) targets, a high-precision space assembly approach based on micro-vision is proposed in this paper. The approach consists of three modules: (i) a posture alignment module based on a multi-vision monitoring model that is designed to align two trans-scale micro-parts in 5DOF while one micro-part is in ten microns and the other one is in hundreds of microns; (ii) an insertion depth control module based on a proposed local deformation detection method to control micro-part insertion depth; (iii) a glue mass control module based on simulation research that is designed to control glue mass quantitatively and to bond micro-parts together. A series of experiments were conducted and experimental results reveal that attitude alignment control error is less than ±0.3°, position alignment control error is less than ±5 µm, and insertion depth control error is less than ±5 μm. Deviation of glue spot diameter is controlled at less than 15 μm. A CFTA was assembled based on the proposed approach, the position error in 3D space measured by computerized tomography (CT) is less than 5 μm, and glue spot diameter at the joint is 56 μm. Through volume calculation by the cone calculation formula, the glue mass is about 23 PL when the cone height is half the diameter.

Optimization-Based Strapdown Attitude Alignment for High-Accuracy Systems: Covariance Analysis With Applications

Strapdown inertial navigation usually relies on the extended Kalman filter (EKF) as the workhorse, which requires a proper initialization of the attitude. The optimization-based alignment (OBA) approach is widely employed to provide the coarse attitude information for the subsequent fine alignment or navigation stage by EKF. However, there still lacks a reliable quality index to assess the attitude accuracy of OBA. To tackle this problem, this article characterizes the OBA attitude error by vector measurement errors. For specific applications, such as the stationary alignment and odometer/GPS-aided in-motion alignment, the errors of vector measurements in OBA are further formulated in terms of raw sensor errors of gyroscopes, accelerometers, and odometer/GPS velocities. The covariance of attitude errors is analytically derived. Simulations and field experiments are performed to verify the covariance analysis.

PolarTracker: Attitude-Aware Channel Access for Floating Low Power Wide Area Networks

Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffers significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> , a novel channel access method for floating LPWAN. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. To support concurrent access of multiple LoRa nodes, an attitude-based slotted-ALOHA protocol is proposed to reduce collision. We implement a prototype of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PolarTracker</i> can efficiently improve the packet reception ratio by 50.6%, compared with ALOHA in LoRaWAN.

Inertial-Based Integration With Transformed INS Mechanization in Earth Frame

This article proposes to use a newly derived transformed inertial navigation system (INS) mechanization to fuse INS with other complementary navigation systems. Through formulating the attitude, velocity, and position as one group state of a group of double direct spatial isometries <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb{S}{\mathbb{E}_2}(3)$</tex-math></inline-formula> , the transformed INS mechanization has proven to be group affine, which means that the corresponding vector error state model will be trajectory-independent. In order to make use of the transformed INS mechanization in inertial-based integration, both the right and left vector error state models are derived. The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial-based integration. Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb{S}{\mathbb{E}_2}(3)$</tex-math></inline-formula> based error state covariance, and feedback correction corresponding to the error state definitions. Extensive Monte Carlo simulations and land vehicle experiments are conducted to evaluate the performance of the derived error state models. It is shown that the most striking superiority of using the derived error state models is their ability to handle the large initial attitude misalignments, which is just the result of log-linearity property of the derived error state models. Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications. Moreover, the derived right error state-space model is also very preferred for long-endurance INS/Odometer integration due to the filtering consistency caused by its less dependence on the global state estimate.

A Trident Quaternion Framework for Inertial-Based Navigation—Part II: Error Models and Application to Initial Alignment

This work deals with error models for trident quaternion framework proposed in the companion paper (Part I) and further uses them to investigate the odometer-aided static/in-motion inertial navigation attitude alignment for land vehicles. By linearizing the trident quaternion kinematic equation, the left and right trident quaternion error models are obtained, which are found to be equivalent to those derived from profound group affine. The two error models are used to design their corresponding extended Kalman filters (EKF), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF). Simulations and field tests are conducted to evaluate their actual performances. Owing to the high estimation consistency, the L/RQEKF converge much faster in the static alignment than the traditional error model-based EKF, even under arbitrary large heading initialization. For the in-motion alignment, the L/RQEKF possess much larger convergence region than the traditional EKF does, although they still require the aid of attitude initialization so as to avoid large initial attitude errors.

Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness

Existing person re-recognition (Re-ID) methods usually suffer from poor generalization capability and over-fitting problems caused by insufficient training samples. We find that high-level attributes, semantic information, and part-based local information alignment are useful for person Re-ID networks. In this study, we propose a person re-recognition network with part-based attribute-enhanced features. The model includes a multi-task learning module, local information alignment module, and global information learning module. The ResNet based on non-local and instance batch normalization (IBN) learns more discriminative feature representations. The multi-task module, local module, and global module are used in parallel for feature extraction. To better prevent over-fitting, the local information alignment module transforms pedestrian attitude alignment into local information alignment to assist in attribute recognition. Extensive experiments are carried out on the Market-1501 and DukeMTMC-reID datasets, whose results demonstrate that the effectiveness of the method is superior to most current algorithms.

Attitude Alignment Method of Solar Telescope Secondary Mirror Based on Aberration Detection

Attitude Alignment Method of Solar Telescope Secondary Mirror Based on Aberration Detection

Research on the Shearer Positioning Method Based on SINS and LiDAR with Velocity and Absolute Position Constraints

Accurate positioning of the shearer with a strapdown inertial navigation system (SINS) is the key technology to realize the automation of the longwall face. Unfortunately, the existing positioning methods have a strong dependence on the attitude accuracy of the SINS. The position errors gradually increase with the drift of the SINS attitude. To reduce the dependence on the SINS attitude and further increase the shearer positioning accuracy, this paper proposes a positioning method based on SINS and light detection and ranging (LiDAR) with velocity and absolute position constraints. A Kalman filter (KF) model based on these constraints was established. Simulation analysis shows that the attitude calibration between the shearer body, SINS and LiDAR, and the initial attitude alignment of the SINS are the keys to determining the shearer positioning accuracy. Even if there are small horizontal bends in the running track of the shearer and the features have small horizontal errors, an excellent positioning effect can still be obtained. In addition, four cutting processes were simulated with a reciprocating travel of 44.6 m and an advance distance of 1.2 m. Compared with the relative positioning method, the positioning accuracy of the proposed method was improved by 37%, 63%, 76%, and 69% from the first to the fourth cutting cycle, respectively, calculated by spherical error probable (SEP) values, and positioning accuracy had a lower dependence on the installation deflection angles between the SINS, the LiDAR, and the SINS attitude accuracy.

Do Relationship- and Self-Oriented Deceptions Impact the Effect of Attitude Alignment on Attraction?

While individuals value honesty in relationships, disagreements can create discomfort and individuals tend to prefer partners who engage in attitude alignment (i.e., shift their attitude toward agreement when disagreement occurs). In order to maintain smooth interactions, partners may even deceive individuals by concealing their continued disagreement. In a 2 (Attitude Alignment Deception) x 2 (Partner Motivation) between-subjects design, we examined whether individuals would evaluate interaction partners who engaged in deceptive attitude alignment less favorably compared to interaction partners who engaged in honest attitude alignment, and we examined whether partner motivation (relationship- vs. self-oriented) impacted this effect. After a brief in-person discussion about a disagreed upon social issue, participants received written feedback, allegedly from their interaction partners, that their partners engaged in attitude alignment that was either honest (i.e., partner came to agree with the individual) or deceptive (i.e., partner pretended to reach agreement with the individual). Participants also received feedback that their partners actions were motivated by either a relationship-orientation (i.e., to be liked by the individual) or self-orientation (i.e., to be viewed as correct on the issue). Individuals evaluated (e.g., were less attracted to) partners who engaged in deceptive (vs. honest) attitude alignment less favorably, but deceptive partners were evaluated more favorably if their deception was relationship- vs. self-oriented.

A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning

The adjustment times of the attitude alignment are fluctuated due to the fluctuation of the contact force signal caused by the disturbing moments in the compliant peg‐in‐hole assembly. However, these fluctuations are difficult to accurately measure or definition as a result of many uncertain factors in the working environment. It is worth noting that gravitational disturbing moments and inertia moments significantly impact these fluctuations, in which the changes of the peg concerning the mass and the length have a crucial influence on them. In this paper, a visual grasping strategy based on deep reinforcement learning is proposed for peg‐in‐hole assembly. Firstly, the disturbing moments of assembly are analyzed to investigate the factors for the fluctuation of assembly time. Then, this research designs a visual grasping strategy, which establishes a mapping relationship between the grasping position and the assembly time to improve the assembly efficiency. Finally, a robotic system for the assembly was built in V‐REP to verify the effectiveness of the proposed method, and the robot can complete the training independently without human intervention and manual labeling in the grasping training process. The simulated results show that this method can improve assembly efficiency by 13.83%. And, when the mass and the length of the peg change, the proposed method is still effective for the improvement of assembly efficiency.

A robust in-motion attitude alignment method for odometer-aided strapdown inertial navigation system.

A significant issue of land vehicle navigation is in-motion attitude alignment of the odometer (OD)-aided strapdown inertial navigation system (SINS). The consecutive OD outliers can occur due to sudden wheel slipping and skidding while vehicle maneuvering. They seriously reduce the robustness and precision of attitude alignment. In this paper, we investigate a robust in-motion attitude alignment method for the OD-aided SINS. The method consists of in-motion coarse alignment and in-motion fine alignment. In the in-motion coarse alignment process, we developed Huber's M-estimation and integral formula based robust Kalman filter (HRKF/IF-CA), which can restrain the interference of consecutive OD outliers on reconstructed observation vectors. Thus, HRKF/IF-CA can contribute to better coarse attitude results. The next process is in-motion fine alignment. Under the popular repeated backtracking scheme, we investigate HRKF based fine alignment (HRKF-FA) with the SINS/OD summed measurement model. HRKF-FA can refine attitude alignment and restrain the interference of consecutive OD outliers simultaneously. Finally, the proposed method is evaluated by simulation and vehicle test. The attitude alignment results show that this method can achieve reasonable attitude results, and the interference of consecutive OD outliers caused by sudden wheel slipping and skidding can be greatly restrained.

Robust attitude alignment in multispacecraft systems with stochastic links failure

Attitude consensus problem in multispacecraft systems is addressed. Despite existing results devoted to attitude consensus control of spacecraft under deterministic communication, we propose a control strategy under which a team of spacecraft reaches almost sure consensus on an attitude, while communication links stochastically fail over time. Moreover, the robustness of the control strategy in the presence of parameter uncertainties is guaranteed. By quaternion formulation of the spacecraft attitudes dynamics, a nonlinear attitude consensus control strategy is developed such that by invoking the generalized invariance principle and the super-martingales convergence theorem, the mentioned objectives are achieved. Simulation results for a team of six spacecraft validate the obtained results.

Integrated navigation approaches of vehicle aided by the strapdown celestial angles

The integrated navigation method based on star sensor celestial angles (altitude angle and azimuth angle) is proposed to serve the need for rapidly responsive, reliable, and precise of a hypersonic vehicle under a sophisticated environment. An integrated navigation algorithm suitable for large azimuth misalignment is established under launching point inertial coordinate and local geographical coordinate system based on altitude angle and azimuth angles. Meanwhile, a Bayesian method for data dropouts aided by the strapdown celestial angles is presented for the rapid variability in the celestial star angle with active galaxies. A nonlinear Bayesian filter is applied to implement the simulation on account of the nonlinear feature of the state and measurement equations. The simulation results showed that the Bayesian method for integrated navigation data dropouts could be accomplished by altitude angle and azimuth angle aiding in both launching point inertial coordinate and local geographical coordinate systems, which converge to 1′ in 10 s. The method indicated that the integrated navigation significant errors derived from initial localization and initial attitude alignment could be modified by the strapdown inertial navigation system (SINS) supported by the star sensor’s celestial angle in the local geographic coordinate system in the early launch stage. For the seconds of the flight phase, the integrated navigation aided by celestial angles in the launching point inertial coordinate system was guaranteed for the feasibility and validity. During the flight, the feasibility and validity of integrated navigation were guaranteed aided by celestial angles in launching point inertial coordinate system.