Search results

The purpose of this paper is to introduce a methodology aimed to detect debonding induced by low impacts energies in typical aeronautical structures. The methodology is based on high frequency sensors/actuators system simulation and the application of elliptical triangulation (ET) and probability ellipse (PE) methods as damage detector. Numerical and experimental results on small-scale stiffened panels made of carbon fiber-reinforced plastic material are discussed.

The damage detection methodology is based on high frequency sensors/actuators piezoceramics system enabling the ET and the PE methods. The approach is based on ultrasonic guided waves propagation measurement and simulation within the structure and perturbations induced by debonding or impact damage that affect the signal characteristics.

The work is focused on debonding detection via test and simulations and calculation of damage indexes (DIs). The ET and PE methodologies have demonstrated the link between the DIs and debonding enabling the identification of position and growth of the damage.

The debonding between two structural elements caused in manufacturing or in-service is very difficult to detect, especially when the components are in low accessibility areas. This criticality, together with the uncertainty of long-term adhesive performance and the inability to continuously assess the debonding condition, induces the aircrafts’ manufacturers to pursuit ultraconservative design approach, with in turn an increment in final weight of these parts. The aim of this research’s activity is to demonstrate the effectiveness of the proposed methodology and the robustness of the structural health monitoring system to detect debonding in a typical aeronautical structural joint.

The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve.

Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution.

By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method.

The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

The purpose of this paper is to evaluate the safety of formation flying satellites, and propose a method for practical collision monitoring and collision avoidance manoeuvre.

A general formation description method based on relative orbital elements is proposed, and a collision probability calculation model is established. The formula for the minimum relative distance in the crosstrack plane is derived, and the influence of J2 perturbation on formation safety is analyzed. Subsequently, the optimal collision avoidance manoeuvre problem is solved using the framework of linear programming algorithms.

The relative orbital elements are illustrative of formation description and are easy to use for perturbation analysis. The relative initial phase angle between the in‐plane and cross‐track plane motions has considerable effect on the formation safety. Simulations confirm the flexibility and effectiveness of the linear programming‐based collision avoidance manoeuvre method.

The proposed collision probability method can be applied in collision monitoring for the proximity operations of spacecraft. The presented minimum distance calculation formula in the cross‐track plane can be used in safe configuration design. Additionally, the linear programming method is suitable for formation control, in which the initial and terminal states are provided.

The relative orbital elements are used to calculate collision probability and analyze formation safety. The linear programming algorithms are extended for collision avoidance, an approach that is simple, effective, and more suitable for on‐board implementation.

Because of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.

The authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

The experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.

A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments.

Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR.

The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments.

In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

The purpose of this paper is to perform a comparative study of two propagation models and a prediction of proximity distances among the space objects based on the two-line element set (TLEs) data, which identifies potentially risky approaches and is used to compute the probability of collision among the spacecrafts.

At first, the proximities are estimated for the mentioned satellites using a precise propagation model and based on a one-month simulation. Then, a study is performed to determine the probability of collision between two satellites using a formulation which takes into account the object sizes, covariance data and the relative distance at the point of closest approach. Simplifying assumptions such as a linear relative motion and normally distributed position uncertainties at the predicted closest approach time are applied in estimation.

For the case of Iridium-Cosmos collision and the prediction of a closest approach using available TLE orbital data and a propagation model which takes into account the effects of the earth’s zonal harmonics and drag atmospheric, the maximum probability of about 2 × 10 −6 was obtained, which can indicate the necessity of enacting avoidance maneuvers regarding the defined a probability threshold by satellite’s owner.

The contribution of this paper is to analyze and simulate the 2009 prominent collision between the Cosmos2251 and Iridium33 satellite by modeling their orbit propagation, predicting their closest approaches and, finally, assessing the risk of the possible collision. Moreover, an enhanced orbit determination can be effective to achieve an accurate assessment of the ongoing collision threat to active spacecrafts from orbital debris and preventing, if necessary, the hazards thereof.

This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.

The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.

The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.

The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.

Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique.

The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates.

The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully.

Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

Uncertainty can arise for a manipulator because its motion can deviate unpredictably from the assumed dynamical model and because sensors might provide information regarding the system state that is imperfect because of noise and imprecise measurement. This paper aims to propose a method to estimate the probable error ranges of the entire trajectory for a manipulator with motion and sensor uncertainties. The aims are to evaluate whether a manipulator can safely avoid all obstacles under uncertain conditions and to determine the probability that the end effector arrives at its goal area.

An effective, analytical method is presented to evaluate the trajectory error correctly, and a motion plan was executed using Gaussian models by considering sensor and motion uncertainties. The method used an integrated algorithm that combined a Gaussian error model with an extended Kalman filter and a linear–quadratic regulator. Iterative linearization of the nonlinear dynamics was used around every section of the trajectory to derive all of the prior probability distributions before execution.

Simulation and experimental results indicate that the proposed trajectory planning method based on the motion and sensor uncertainties is indeed highly convenient and efficient.

The proposed approach is applicable to manipulators with motion and sensor uncertainties. It helps determine the error distribution of the predefined trajectory. Based on the evaluation results, the most appropriate trajectory can be selected among many predefined trajectories according to the error ranges and the probability of arriving at the goal area. The method has been successfully applied to a manipulator operating on the Chinese Space Station.

This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.