scholarly journals Variable stiffness identification and configuration optimization of industrial robots for machining tasks

2020 ◽  
Author(s):  
Jiachen Jiao ◽  
Wei Tian ◽  
Lin Zhang ◽  
Bo Li ◽  
Junshan Hu ◽  
...  
Keyword(s):  
Variable Stiffness ◽  
Optimization Methods ◽  
Optimization Method ◽  
Industrial Robots ◽  
Axial Stiffness ◽  
Configuration Optimization ◽  
Positional Accuracy ◽  
Stiffness Identification ◽  
Robot Stiffness ◽  
High Flexibility

Abstract Industrial robots are increasingly used in machining tasks because of their high flexibility and intelligence. However, the low structural stiffness of the robot seriously affects the positional accuracy and machining quality of robot operation equipment. Studying robot stiffness characteristics and optimization methods is an effective way to improve robot stiffness performance. Accordingly, aiming at the poor accuracy of stiffness modeling caused by approximating stiffness of each joint as constant, a variable stiffness identification method is proposed based on space gridding. Then, a task-oriented axial stiffness evaluation index is proposed to realize quantitative assessment of the stiffness performance in the machining direction. Besides, by analyzing the redundant kinematic characteristics of the robot machining system, a configuration optimization method is further come up with to maximize the index. For a large number of points or trajectory processing tasks, a configuration smoothing strategy is proposed to achieve fast acquisition of optimized configurations. Finally, experiments on a KR500 robot are conducted to verify the feasibility and validity of proposed stiffness identification and configuration optimization methods.

Synthesis of Gear-Linkage Mechanisms by Topology Optimization

2018 ◽  
Author(s):  
Neung Hwan Yim ◽  
Seok Won Kang ◽  
Yoon Young Kim
Keyword(s):  
Topology Optimization ◽  
Design Space ◽  
Synthesis Method ◽  
Variable Stiffness ◽  
Optimization Methods ◽  
Optimization Method ◽  
Planar Mechanisms ◽  
Gear Design ◽  
Linkage Design ◽  
Gradient Based

This work is concerned with a new mechanism synthesis method for the simultaneous determination of the type, number and dimension of mechanisms by topology optimization. Earlier topology optimization methods can synthesize linkage mechanisms that consist only of links and joints. The proposed synthesis method is a gradient-based topology optimization method useful for the synthesis of planar mechanisms consisting of linkages and gears. To formulate the topology optimization based method, we propose two superposed design spaces as a ground structure: the linkage and gear design spaces. The gear design space is discretized by newly proposed gear blocks while the linkage design space by rigid blocks. The zero-length springs with variable stiffness are used to control the connectivity of blocks, which in turns determines the configuration of the synthesized mechanism. After the proposed topology-optimization-based synthesis formulation is presented, its effectiveness and validity are checked with various synthesis examples.

Configuration Optimization of Truss Structures Using Harmony Search Heuristic Algorithm

2007 ◽  
Vol 26-28 ◽  
pp. 793-796 ◽  
Author(s):  
Kang Seok Lee ◽  
Jin Kyu Song
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Random Search ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimum ◽  
Truss Structures ◽  
Configuration Optimization ◽  
Mathematical Algorithms

Most structural optimization methods are based on mathematical algorithms that require substantial gradient information. The selection of the starting values is also important to ensure that the algorithm converges to the global optimum. This paper describes a new structural configuration optimization method based on the harmony search (HS) meta-heuristic algorithm. The HS algorithm does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. A benchmark truss example is presented to demonstrate the effectiveness and robustness of the proposed approach compared to other optimization methods. Results reveal that the proposed approach is capable of solving configuration optimization problems, and may yield better solutions than those obtained using earlier methods.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Comparison between Optimization Method and Experience-Based Method in Soil Conservation

2013 ◽  
Vol 726-731 ◽  
pp. 3811-3817
Author(s):  
Yuan Feng ◽  
Ji Xian Wang
Keyword(s):  
Slope Stability ◽  
Soil Conservation ◽  
Computer Aided Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Interaction Function ◽  
The Standard Model ◽  
Computer Aided ◽  
Slice Method ◽  
Aided Design

The analysis of the slope stability is important in soil conservation. To analyze the slope stability, optimization methods were coded and compared with the traditional experience-based methods. Furthermore, the results were visualized in the program, so that the user can easily check the results and can designate an area, in which the program seeks the center and radius of the most hazardous slide arc. Moreover, the graphic interaction function was implemented in the program. In addition, the Standard Model One, recommended by ACAD (The Association for Computer Aided Design), was calculated by the program, of which the results (safety factor Ks=0.95~0.96) were smaller than the official recommend value (Ks=1). It is because that the traditional slice method, which neglects the normal stress and shear stress between the slices, was applied for calculation of Ks.

Nonsmooth Optimization Method for Image Restoration

1995 ◽  
Author(s):  
Kazufumi Ito ◽  
Karl Kunisch
Keyword(s):  
Image Restoration ◽  
Nonsmooth Optimization ◽  
Numerical Optimization ◽  
Augmented Lagrangian ◽  
Energy Criterion ◽  
Optimization Methods ◽  
Optimization Method ◽  
Augmented Lagrangian Methods ◽  
Active Set ◽  
First Order

Abstract In this paper we discuss applications of the numerical optimization methods for nonsmooth optimization, developed in [IK1] for the variational formulation of image restoration problems involving bounded variation type energy criterion. The Uzawa’s algorithm, first order augmented Lagrangian methods and Newton-like update using the active set strategy are described.

Design Optimization Method for MR-Brake Actuators

2011 ◽  
Author(s):  
Ozan G. Erol ◽  
Hakan Gurocak ◽  
Berk Gonenc
Keyword(s):  
Optimal Solution ◽  
Volume Ratio ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Friction Torque ◽  
Element Analysis ◽  
Taguchi Design Of Experiments ◽  
Variable Friction ◽  
Electronically Controllable

MR-brakes work by varying viscosity of a magnetorheological (MR) fluid inside the brake. This electronically controllable viscosity leads to variable friction torque generated by the actuator. A properly designed MR-brake can have a high torque-to-volume ratio which is quite desirable for an actuator. However, designing an MR-brake is a complex process as there are many parameters involved in the design which can affect the size and torque output significantly. The contribution of this study is a new design approach that combines the Taguchi design of experiments method with parameterized finite element analysis for optimization. Unlike the typical multivariate optimization methods, this approach can identify the dominant parameters of the design and allows the designer to only explore their interactions during the optimization process. This unique feature reduces the size of the search space and the time it takes to find an optimal solution. It normally takes about a week to design an MR-brake manually. Our interactive method allows the designer to finish the design in about two minutes. In this paper, we first present the details of the MR-brake design problem. This is followed by the details of our new approach. Next, we show how to design an MR-brake using this method. Prototype of a new brake was fabricated. Results of experiments with the prototype brake are very encouraging and are in close agreement with the theoretical performance predictions.

scholarly journals Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Evolutionary Algorithms ◽  
Resting State ◽  
Optimization Methods ◽  
Resting State Fmri ◽  
Optimization Method ◽  
Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

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