Study on an Underwater Flexible Manipulator Based on Hydraulic Drive

2020 ◽  
Author(s):  
Xin Li ◽  
He Xu ◽  
Chen Yang ◽  
Haihang Wang ◽  
Fengshu Yu
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Three Dimensional ◽  
Hydraulic Drive ◽  
Flexible Manipulator ◽  
Artificial Muscles ◽  
Flexible Robot ◽  
Hydraulic Power ◽  
The Neural Network ◽  
Real Scene

Abstract The underwater flexible robot is a field of continuous exploration and innovation. An underwater flexible manipulator with the functions of bending and grasping is presented in this paper, which is driven by the water hydraulic. The flexible manipulator is consisted mainly of three sets of transverse and three sets of longitudinal Mckibben artificial muscles (MAM) equidistantly arranged. The motions of the manipulator were driven by accurately controlling the length of each MAM that was changed by controlling the internal pressure, which was provided by the hydraulic power subsystem. The flexible manipulator was controlled remotely by the control subsystem. The inverse kinematics of the flexible manipulator was studied based on the neural network in this paper. The feasibility of the neural network inverse kinematics was proved by the data analysis. The three-dimensional virtual model of the flexible manipulator was projected into the captured real scene by the augmented reality (AR) technology to judge the bending degree of the manipulator operation, which could be seen in the experiment image.

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

scholarly journals A convolutional neural network for defect classification in Bragg coherent X-ray diffraction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Bruce Lim ◽  
Ewen Bellec ◽  
Maxime Dupraz ◽  
Steven Leake ◽  
Andrea Resta ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diffraction Pattern ◽  
Three Dimensional ◽  
X Ray Diffraction ◽  
Coherent Diffraction Imaging ◽  
The Neural Network ◽  
Diffraction Imaging ◽  
Diffraction Patterns ◽  
Defect Recognition

AbstractCoherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials. These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type (screw or edge). Our study paves the way for defect-recognition in 3D coherent diffraction patterns for material science.

MPID Control Tuning for a Flexible Manipulator Using a Neural Network

2010 ◽  
Vol 22 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Tamer Mansour ◽  
◽  
Atsushi Konno ◽  
Masaru Uchiyama
Keyword(s):  
Neural Network ◽  
Vibration Control ◽  
Learning Algorithm ◽  
The Other ◽  
Gradient Algorithm ◽  
Flexible Manipulator ◽  
Computational Time ◽  
Speed Of Response ◽  
Control Gain ◽  
The Neural Network

This paper studies the use of neural networks as a tuning tool for the gain in Modified Proportional-Integral-Derivative (MPID) control used to control a flexible manipulator. The vibration control gain in the MPID controller has been determined in an empirical way so far. It is a considerable time consuming process because the vibration control performance depends not only on the vibration control gain but also on the other parameters such as the payload, references and PD joint servo gains. Hence, the vibration control gain must be tuned considering the other parameters. In order to find optimal vibration control gain for the MPID controller, a neural network based approach is proposed in this paper. The proposed neural network finds an optimum vibration control gain that minimizes a criteria function. The criteria function is selected to represent the effect of the vibration of the end effector in addition to the speed of response. The scaled conjugate gradient algorithm is used as a learning algorithm for the neural network. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the neural network appears in the reduction of the computational time and the ability to tune the gain with different loading condition.

scholarly journals DEVELOPMENT OF ALGORITHMIC SUPPORT AND A DEDICATED DEVICE FOR IMAGING THE INTERNAL ORGANS OF THE PATIENT ACCORDING TO MRI AND CT SCANNERS

2019 ◽  
Vol 22 (6) ◽  
pp. 189-197
Author(s):  
E. S. Sirota ◽  
M. I. Truphanov
Keyword(s):  
Neural Network ◽  
Three Dimensional ◽  
Color Difference ◽  
Learning Criterion ◽  
Average Value ◽  
Advantages And Disadvantages ◽  
The Neural Network ◽  
Training Stage ◽  
Algorithmic Support

In work the algorithm of restoration of the images damaged as a result of influence of noise of various nature is considered. The advantages and disadvantages of the existing approaches, as well as the prospects of using artificial neural networks, are noted. A double-layer neural network is used as an image restoration tool, and it is assumed that the location of the damaged pixels is known. A neuron is represented as a 3x3 array, where each element of the array has a pixel color value that corresponds to the value of that color in the palette. The neural network is trained on intact images, while the color difference of pixels acts as a learning criterion. For a more accurate restoration, it is recommended at the training stage to select images similar in color to damaged ones. At the recovery stage, neurons (3x3) are formed around the damaged pixels, so that the damaged pixel is located in the middle of the neuron data array. The damaged pixel is assigned a neuron value depending on the average value of the weights matrix. An algorithm for the restoration of pixels, as well as its software implementation. The simulation was carried out in the RGB palette separately for each channel. To assess the quality of the recovery were selected groups of images with varying degrees of damage. Unlike existing solutions, the algorithm has the simplicity of implementation. The  research results show that regardless of the degree of damage (within 50%), about 70% of damaged pixels are restored. Further studies suggest a modification of the algorithm to restore images with enlarged areas of damage, as well as adapting it to restore three-dimensional images.

scholarly journals Reconstruction of 3D Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3D Models from ShapeNetCore Dataset

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1553 ◽  
Author(s):  
Audrius Kulikajevas ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Sanjay Misra
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
A Priori ◽  
Three Dimensional ◽  
Qualitative Evaluation ◽  
3D Models ◽  
Depth Sensors ◽  
The Neural Network ◽  
Artificial Neural Network Ann ◽  
Commercial Applications

Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.

Obstacle avoidance inverse kinematics solution of redundant robots by neural networks

Robotica ◽  
1997 ◽  
Vol 15 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Ziqiang Mao ◽  
T. C. Hsia
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Solution Technique ◽  
Training Algorithms ◽  
Neural Network Approach ◽  
Work Space ◽  
The Neural Network ◽  
Free Case

This paper investigates the neural network approach to solve the inverse kinematics problem of redundant robot manipulators in an environment with obstacles. The solution technique proposed requires only the knowledge of the robot forward kinematics functions and the neural network is trained in the inverse modeling manner. Training algorithms for both the obstacle free case and the obstacle avoidance case are developed. For the obstacle free case, sample points can be selected in the work space as training patterns for the neural network. For the obstacle avoidance case, the training algorithm is augmented with a distance penalty function. A ball-covering object modeling technique is employed to calculate the distances between the robot links and the objects in the work space. It is shown that this technique is very computationally efficient. Extensive simulation results are presented to illustrate the success of the proposed solution schemes. Experimental results performed on a PUMA 560 robot manipulator is also presented.

Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

2012 ◽  
Vol 510 ◽  
pp. 723-728 ◽  
Author(s):  
Liang Cheng ◽  
Hui Chang ◽  
Bin Tang ◽  
Hong Chao Kou ◽  
Jin Shan Li
Keyword(s):  
Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Back Propagation ◽  
Three Dimensional ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Ann Model ◽  
The Neural Network ◽  
Hidden Layer

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

Controlling of the 3D Space Trajectory of the Multi Robots Applications by Using the Proper Iterative Pseudo Inverse Jacobian Neural Network Matrix Method

2016 ◽  
Vol 841 ◽  
pp. 227-233
Author(s):  
Adrian Olaru ◽  
Serban Olaru ◽  
Niculae Mihai ◽  
Doru Bardac
Keyword(s):  
Neural Network ◽  
Matrix Method ◽  
Inverse Kinematics ◽  
Parallel Robot ◽  
Coupled Method ◽  
Space Curves ◽  
3D Space ◽  
The Neural Network ◽  
Inverse Kinematics Problem ◽  
Pseudo Inverse

In many applications we used the multi robots with the central coordination of the 3D space trajectory. In the controlling of the space movement of the end effecter of the all robots from this type of applications and the robot’s joints one of the most important problem is to solve the forward and inverse kinematics, that is different from the single robot application. It is important to know with the extreme precision the joints relative displacements of all robots. One of the most precise method to solve the inverse kinematics problem in the robots with redundant chain is the complex coupled method of the neural network with Iterative Jacobian Pseudo Inverse method. In this paper was proposed and used the proper coupled method Iterative Pseudo Inverse Jacobian Matrix Method (IPIJMM) with Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN-TDRL). The paper contents the mathematical matrix model of the forward kinematics of multiple robots applications, mathematical model of the proper iterative algorithm and all proper virtual LabVIEW instrumentation, to obtain the space conventional and unconventional curves in different Euller planes for one case study of three simultaneously robots movement with extreme precision of the end-effecter less than 0.001mm. The paper shown how can be changed the multi robots application in to one application with parallel robot structure with three independent robots. The presented method and the virtual instrumentations (VI) are generally and they can be used in all other robots application and for all other conventional and unconventional space curves.

Three-dimensional flexural-joint stiffness analysis of flexible manipulator arms

Robotica ◽  
1988 ◽  
Vol 6 (3) ◽  
pp. 203-212 ◽  
Author(s):  
A. Meghdari ◽  
M. Shahinpoor
Keyword(s):  
Stiffness Matrix ◽  
Three Dimensional ◽  
Joint Stiffness ◽  
Beam Theory ◽  
Flexible Manipulator ◽  
Robotic Arm ◽  
Flexible Robot ◽  
Stiffness Analysis ◽  
Stiffness Matrices ◽  
Stiffness Properties

SUMMARYThis paper presents a complete derivation of the combined flexural-joint stiffness matrix and the elastic deformation field of flexible manipulator arms treated in a three-dimensional fashion. The stiffness properties are derived directly from the differential equations used in the engineering beam theory. The expressions developed here can readily be used in the modeling, control and design of light weight flexible robot manipulators. A two-link arm is used to formulate these expressions and the results can be generalized to n–link manipulators. The stiffness matrix for a robotic link element in 3-D is of the order of 12 X 12, and for an n–link robotic arm the total elemental and system stiffness matrices will be of the order of the (12n X 12n) and 6(n + 1) X 6(n + 1), respectively.

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