A new force-depth model for robotic abrasive belt grinding and confirmation by grinding of the Inconel 718 alloy

Robotic abrasive belt grinding has been successfully applied to the grinding and polishing of aerospace parts. However, due to the flexible characteristics of robotic abrasive belt grinding and the time-varying characteristics of the polishing contact force, as well as the plastic and difficult-to-machine material properties of Inconel 718 alloy, it is very difficult to control the actual removal depth and force of the polished surface, which brings great challenges to robot automatic polishing. Therefore, the relationship between the grinding force and the grinding depth in the robotic abrasive belt grinding is analyzed in detail, the robot machining pose error model considering the deformation of the grinding head is established, and the Inconel 718 alloy machining experiment of the robotic abrasive belt grinding is designed. The mapping relationship between the grinding force and the grinding depth is obtained, and the grinding force ratio in the downgrinding and upgrinding mode is discussed. The experimental and theoretical comparisons results show that with the increase of the grinding depress depth, both the grinding depth and the grinding force show an irregular increasing trend, and the increasing trend of the grinding force (increases by about 344.44%–445.45%) is obviously greater than that of the grinding depth (increases by about 52.94%). When the grinding depress depth is large (greater than 3 mm), the feed direction force and the normal force appear obvious secondary pressure peaks at the beginning and end of grinding, which has not been seen in previous studies. In addition, regardless of whether it is downgrinding or upgrinding, the grinding force ratio decreases with the increase of the depress depth, and the grinding force ratio of downgrinding (average 0.668) is smaller than that of upgrinding (average 0.724). This study provides a reference for robotic abrasive belt grinding, and the surface quality of Inconel 718 alloy of robotic abrasive belt grinding can be further improved through the optimization of force and depth.     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

Integrated mathematical optimisation approach for the tower crane hook routing problem to satisfy material demand requests on-site

Optimising decisions around the location and operation of tower cranes can improve the workflow in construction projects. Traditionally, the location and allocation problems involved in tower crane operations in the literature have been solved separately from the assignment of material supply points to demand points and the scheduling of the crane’s activity sequence across supply and demand points on a construction site. To address the gap, this paper proposes a binary integer programming problem, where location of the tower crane, allocation of supply points to material-demanding regions, and routing of hook of the crane based on activity sequencing of the hook across supply and material-demanding regions on site are optimised. The novelty in this work is in the way the crane’s activity scheduling is modelled via mathematical programming, based on routing the hook movement to meet material demand, through minimising tower crane operating costs. A realistic case study is solved to assess the validity of the model. The model is contrasted with results obtained from other solving algorithms commonly adopted in the literature, along with a solution proposed by an experienced practitioner. Results indicate that all instances can be solved when compared to other meta-heuristics that fail to achieve an optimum solution. Compared to the solution proposed by the practitioner, the results of the proposed model achieve a 46% improvement in objective function value. Planners should optimise decisions related to the location of the crane, the crane’s hook movement to meet service requests, and supply points’ locations and assignment to material-demanding regions simultaneously for effective crane operations.     

基于顺序扩展一致性包算法的多无人机分布式任务分配

以异构多无人机协同执行复杂的耦合多任务为背景,提出一种求解分布式任务分配问题非死锁的顺序扩展一致性包算法.首先,建立考虑任务载荷资源、任务时序、威胁区等约束条件的时序多任务分配模型;其次,对一致性包算法的任务包构建过程和冲突消解规则进行扩展,并设计一种基于有向图深度优先搜索的方法进行任务方案的死锁检测和修正,以实现无冲突和无死锁的任务分配;然后,将关联任务之间的时序约束转化为软时间窗约束,利用顺序分层的策略进行求解;最后,为了提高任务分配结果的可靠性,采用Dubins曲线路径将航路规划耦合到任务分配中.仿真实验表明,所提出的算法能够快速有效地求解异构多无人机分布式耦合多任务分配问题,具备良好的最优性和时效性.     

An observer-based approach for the projection onto a 2d-curve under movement

Standard path control laws of autonomous vehicles use the shortest distance between the vehicle’s position and the path as a control error. In order to determine this distance, the projection point onto the path needs to be determined continuously. This requires fast algorithms that feature high numerical reliability in the field of vehicle application.This paper presents two different observer-based approaches for the projection problem. The identity observer reconstructs all states of interest for path control. The second one, a reduced observer, only possesses the curve parameter as a state and calculates the other values by algebraic formulas. Both algorithms consider the continuous movement of the vehicle, the run of the curve, and work without any approximation of the curve. Furthermore, they are applicable for arbitrary parameterized smooth curves, guarantee the required numerical stability, have short calculating time, and show good statistical properties. The performance is shown in several simulations as well as under real conditions.     

Improved Adaptive Controller Synthesis of Piecewise‐Affine Systems

This paper considers the adaptive control problem for piecewise affine systems (PWS), a novel synthesis framework is presented based on the piecewise quadratic Lyapunov function (PQLF) instead of the common quadratic Lyapunov function to achieve the less conservatism. First, by designing the projection‐type piecewise adaptive law, the problem of the adaptive control of PWS can be reduced to the control problem of augmented piecewise systems. Then, we construct the piecewise affine control law for augmented piecewise systems in such a way that the PQLF can be employed to establish the stability and performance. In particular, the Reciprocal Projection Lemma is employed to formulate the synthesis condition as linear matrix inequalities (LMIs), which enables the proposed PQLF approach to be numerically solvable. Finally, an engineering example is shown to illustrate the synthesis results.     

Topology optimization based on level set for a flexible multibody system modeled via ANCF

A topology optimization methodology is proposed for the flexible multibody system undergoing both large overall motion and large deformation. The system of concern is modeled via the absolute nodal coordinate formulation. The equivalent static load method is employed to transform the topology optimization of the nonlinear dynamic response of the system into a static one, and evaluated to adapt to the absolute nodal coordinate formulation by splitting the elastic deformations of the flexible components from the overall motions of those components. During the static topology optimization, the material interface is implicitly described as the zero level set of a higher-dimensional scalar function. Then, the semi-implicit level set method with the additive operator splitting algorithm is employed to solve the corresponding Hamilton-Jacobi partial differential equation. In addition, the expert evaluation method of weights based on the grey theory is utilized to define the objective function, and a modified augmented Lagrange multiplier method is proposed to treat the inequality volume constraint so as to avoid the oscillation and drift of the volume. Finally, two numerical examples are provided to validate the proposed methodology.     

Improved learning of I2C distance and accelerating the neighborhood search for image classification

Image-to-class (I2C) distance is a novel measure for image classification and has successfully handled datasets with large intra-class variances. However, due to the lack of a training phase, the performance of this distance is easily affected by irrelevant local features that may hurt the classification accuracy. Besides, the success of this I2C distance relies heavily on the large number of local features in the training set, which requires expensive computation cost for classifying test images. On the other hand, if there are small number of local features in the training set, it may result in poor performance.In this paper, we propose a distance learning method to improve the classification accuracy of this I2C distance as well as two strategies for accelerating its NN search. We first propose a large margin optimization framework to learn the I2C distance function, which is modeled as a weighted combination of the distance from every local feature in an image to its nearest-neighbor (NN) in a candidate class. We learn these weights associated with local features in the training set by constraining the optimization such that the I2C distance from image to its belonging class should be less than that to any other class. We evaluate the proposed method on several publicly available image datasets and show that the performance of I2C distance for classification can significantly be improved by learning a weighted I2C distance function. To improve the computation cost, we also propose two methods based on spatial division and hubness score to accelerate the NN search, which is able to largely reduce the on-line testing time while still preserving or even achieving a better classification accuracy.     

A partition-of-unity based ‘FE-Meshfree’ QUAD4 element with radial-polynomial basis functions for static analyses

Recently, a partition-of-unity (PU) based finite element called ‘FE-Meshfree’ QUAD4 element has been published. That element employed polynomial basis functions for the local approximation (LA). In the present paper, a new FE-Meshfree QUAD4 element employing hybrid radial-polynomial basis functions for the LA is proposed. An advantage of radial-polynomial basis is the freedom from the possible singularity of the moment matrix that could sometimes result with an inappropriate choice of polynomial basis functions. Another advantage is the improved accuracy of finite element solution. The new element has been applied to several linear and nonlinear test problems. The results demonstrate that the new element gives much better performance as compared to the previous FE-Meshfree QUAD4 element with pure polynomial basis. Even with a lower order basis, viz., with just three or four polynomial terms, the present element is capable of giving more accurate solution than the previous element which employs a six-term polynomial basis. The new element also exhibits a much higher degree of tolerance to mesh distortion than the known quadratic elements. Moreover, the linear dependence problem, otherwise associated with many of the partition-of-unity (PU) based elements, is completely eliminated from the present element.