Solution of steering angle based on homogeneous transformation

Steering Angle is related to the design and optimization of steering mechanism and suspension, but it is not equal to the angle of knuckle around kingpin because of the existence of wheel alignment parameters. To calculate the steering angle, this paper derives based on homogeneous transformation its function expression by analyzing spatial geometric relation between the two angles and calculating coordinates related to steering trajectory of wheel center. Then, multi-body model of McPherson suspension with steering system is built and the calculation correctness is verified by comparing the function curve plotted by MATLAB software with the curve simulated by Adams/Car software. The calculation and simulation indicate that between the two angles, there is a ratio which is related to wheel alignment parameters and greater than 1.

A Velocity-Independent DOA Estimator of Underwater Acoustic Signals Via An Arbitrary Cross-Linear Nested Array

In this paper, an estimator for underwater DOA estimation is proposed by using a cross-linear nested array with arbitrary cross angle. The estimator excludes the variation acoustic velocity by deriving the geometric relation of the cross-linear array on the proposed algorithm. Therefore, compared with traditional DOA estimation algorithms via linear array, this estimator eliminates systematic errors caused by the uncertainty factor of the acoustic velocity in the underwater environment. Compared with the traditional acoustic velocity independent algorithm, this estimator uses the nested array and improves the performance of DOA estimation. In addition, the estimator is based on arbitrary angle of the cross-linear array, so it is more flexible in practical applications. Numerical simulations are provided to validate the analytical derivations and corroborate the improved performance in underwater environments where the actual acoustic velocity is not accurate.

A stellar ranging scheme based on the second-order correlation measurement

The stellar ranging is the basis for stellar dynamics research and in-depth research on astrophysics. Parallax method is the most widely used and important basic method for stellar ranging. However, it needs to perform high-precision measurement of the parallax angle and the baseline length together. We aim to propose a new stellar ranging scheme based on second-order correlation that does not require a parallax angle measurement. We hope our solution to be as basic as the parallax method. We propose a new stellar ranging scheme by using the offset of second-order correlation curve signals. The optical path difference between the stars and different base stations is determined by the offset of the second-order correlation curve signals. Then the distance of the stars could be determined by the geometric relation. With the distance to stars out to 10kpc away, our astrometric precision can be better compared to Gaia by simulation. We also design a experiment and successfully prove the feasibility of this scheme. This stellar ranging scheme makes it possible to make further and more accurate stellar ranging without using any prior information and angle measurement.

Mechanical Design of a New Anthropomorphic Robot for Fastening in Wing-Box

The fastener installation in the wing-box faces with narrow space, and it has to be done manually at present. Since manual labor has size constraints, the efficiency is low, and there may be assembly quality instability, it urgently needs automation. Automatic fastening assembly using a robot undoubtedly is an appropriate solution. The existing industrial robots, snake robots, humanoid robots can not meet the fastening assembly requirements in the wing-box. We develop a new anthropomorphic robot with multiple links to perform the inner fastening. A prismatic pair is employed to fit the arm links entering into the wing-box. A shaft with 360 degrees rotation liked human shoulder is introduced to meet the circumferential positioning around the process hole. Arm links are used for robotic end effector reaching the local fastening site. Based on the limitation of assembly position in the wing-box, the link lengths are considered and determined. By using the geometric relation with the link lengths, the joint angle variables are presented. Then, S shape arm link is designed for the compact requirement and the dimensions are determined based on the cross-section of human arm. Finally, stable frame structure is set up through the rear door frame and the bridge beam, and the whole robot is integrated.

HDPL: a hybrid descriptor for points and lines based on graph neural networks

Purpose This paper aims to design a novel feature descriptor to improve the performance of feature matching in challenge scenes, such as low texture and wide-baseline scenes. Common descriptors are not suitable for low texture scenes and other challenging scenes mainly owing to encoding only one kind of features. The proposed feature descriptor considers multiple features and their locations, which is more expressive. Design/methodology/approach A graph neural network–based descriptors enhancement algorithm for feature matching is proposed. In this paper, point and line features are the primary concerns. In the graph, commonly used descriptors for points and lines constitute the nodes and the edges are determined by the geometric relationship between points and lines. After the graph convolution designed for incomplete join graph, enhanced descriptors are obtained. Findings Experiments are carried out in indoor, outdoor and low texture scenes. The experiments investigate the real-time performance, rotation invariance, scale invariance, viewpoint invariance and noise sensitivity of the descriptors in three types of scenes. The results show that the enhanced descriptors are robust to scene changes and can be used in wide-baseline matching. Originality/value A graph structure is designed to represent multiple features in an image. In the process of building graph structure, the geometric relation between multiple features is used to establish the edges. Furthermore, a novel hybrid descriptor for points and lines is obtained using graph convolutional neural network. This enhanced descriptor has the advantages of both point features and line features in feature matching.

IMPROVING RANSAC FEATURE MATCHING BASED ON GEOMETRIC RELATION

Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization and Mapping (VSLAM), and vision-based localization and navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection and rejection are important steps in any vision application. RANSAC has been a well-established approach for outlier detection. The outlier ratio and the number of required correspondences used in RANSAC determine the number of iterations needed, which ultimately, determines the computation time. We propose a simple algorithm (GR_RANSAC) based on the two-dimensional spatial relationships between points in the image domain. The assumption is that the distances and bearing angles between the 2D feature points should be similar in images with small disparity, such as the case for video image sequences. In the proposed approach, the distances and angles are measured from a reference point in the first image and its correspondence in the other image, and the points with any significant differences are considered as outliers. This process can pre-filter the matched points, and thus increase the inliers’ ratio. As a result, GR_RANSAC can converge to the correct hypothesis in fewer trial runs than ordinary RANSAC.

Extended galactic rotational velocity profiles in f(R) gravity background

An attempt has been made to explore the geometric effects of f(R) action on the galactic dynamics under the weak field approximation. The rotational velocity is calculated beyond the Einstein’s geometric theory of gravity. It is inspired by the cosmological geometric relation obtained in the power-law f(R) gravity model in vacuum. We analyse the action with a small positive deviation from the Einstein–Hilbert gravity action (taking R as $$f(R)\propto R^{1+\delta }$$f(R)∝R1+δ) at the galactic scales for the explanation of the flatness paradox associated with the clustered galactic dark matter. We obtain the contribution of a dynamical f(R) cosmological background geometry on accelerating the test mass. Furthermore, the integrated effective acceleration of the test mass due to a massive spherically symmetric source in f(R) background is calculated via the study of geodesics for the suitable spacetime metric and an equation for the effective rotational velocity has been developed. We test the viability of the proposed model by tracing the motion of a test mass far from the disk of galactic matter for smaller $$\delta $$δ. The possible galactic rotational velocity curves in f(R) background are discussed for the formula obtained with $$\delta<< 1$$δ<<1. We also obtain constraints on $$\delta $$δ$$O(10^{-6})$$O(10-6) confirmed by observations.

Geometry-based finger kinematic models for joint rotation configuration and parameter estimation

In this work, geometry-based finger kinematic models for joint rotation configuration are proposed. The purpose of the work is to provide an effective means of describing an individual-specific finger motion during flexion or extension movements as precisely as possible. Based on the finger’s geometric postures that are observed when fingers naturally grasp a cylindrical object with a circular cross-section, its geometric relation between each phalanx of a finger and the object is extracted, and forms of contact between them are taken into consideration to secure more degrees of freedom for representing finger motions and are parameterized in the model development. A parameter identification approach is adopted to find model parameters that can be used to describe an individual-specific grasping style. For the validation of the proposed models, a set of optical motion capture experiments is performed. From the simulation study, one can see that the models provide one of the feasible and viable solutions to imitate the human finger’s flexion and/or extension movements.