Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

In this paper, we present an efficient method based on geometric algebra for computing the solutions to the inverse kinematics problem (IKP) of the 6R robot manipulators with offset wrist. Due to the fact that there exist some difficulties to solve the inverse kinematics problem when the kinematics equations are complex, highly nonlinear, coupled and multiple solutions in terms of these robot manipulators stated mathematically, we apply the theory of Geometric Algebra to the kinematic modeling of 6R robot manipulators simply and generate closed-form kinematics equations, reformulate the problem as a generalized eigenvalue problem with symbolic elimination technique, and then yield 16 solutions. Finally, a spray painting robot, which conforms to the type of robot manipulators, is used as an example of implementation for the effectiveness and real-time of this method. The experimental results show that this method has a large advantage over the classical methods on geometric intuition, computation and real-time, and can be directly extended to all serial robot manipulators and completely automatized, which provides a new tool on the analysis and application of general robot manipulators.

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Solution of Inverse Kinematics for 6R Robot Manipulators With Offset Wrist Based on Geometric Algebra

Inverse kinematic is one of the most interesting problems of industrial robot. The inverse kinematics problem in robotics is about the determination of joint angles for a desired Cartesian position of the end effector. It comprises of the computation need to find the joint angles for a given Cartesian position and orientation of the end effectors to control a robot arm. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network is one such technique which can be gainfully used to yield the acceptable results. This paper proposes a structured artificial neural network (ANN) model to find the inverse kinematics solution of a 4-dof SCARA manipulator. The ANN model used is a multi-layered perceptron neural network (MLPNN), wherein gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that multi-layered perceptron neural network gives minimum mean square error.

http://ijra.iaescore.com/index.php/IJRA/article/download/933/607

A Neural Network Approach for Inverse Kinematic of a SCARA Manipulator

Finding the better solution of the inverse kinematics problem, with the minimum of the trajectory errors, is very difficult because there are many variable parameters and many redundant solutions. The presented paper show the assisted solving of the inverse kinematics with the goal to minimize the final end-effector trajectory errors, by optimizing the distance between the and-effector final position and the target. All obtained results were been verified by applying the proper forward kinematics virtual LabVIEW instrumentation. The paper tries to answer at the inverse kinematics problem for one known mathematical trajectory and identifying the cinematic errors after the establishing and applying the proper assisted solving method using the Cycle Coordinate Descent Method coupled to the proper Neural Network Sigmoid Bipolar Hyperbolic Tangent (CCDM-SBHTNN). We were shown one complete study case to obtain one circle space trajectory using one arm type robot fixed on the ceiling. The presented method is general and can be used in all other robots types and in all other conventional and unconventional space curves.

Proper Assisted Research Method Solving of the Robots Inverse Kinematics Problem

Composite is one of the most widely used industrial materials because of high strength, low weight, and high corrosion resistance properties. Different parts of composite structures are normally joined using adhesives or fasteners that are prone to defects and damages. A reliable method for prediction of the defect location is needed for an efficient structural health monitoring (SHM) process. Heterodyne effect is recently utilized for damage detection in the bonding zone of composite structures where debonding is expected to change the linear characteristics of the system into nonlinear characteristics. This paper briefly introduces this novel defect locating approach in composite plates using the heterodyne effect. For the first time, an Artificial Neural Network methodology is utilized with heterodyne effect method to find the defect location in composite plates. The main objective of this article is to develop a neural network based methodology for prediction of damage location, particularly for the bond inspection of composite plates.

Prediction of damage location in composite plates using artificial neural network modeling

In the optimization of the trajectory or of the guidance of mobile robots one of the more important things is to assure one small difference between the output data of the system and the target This paper show how on-line will be possible to establish one convergence way to the target without any influences of the input data or initial conditions of the weights or biases The paper show the general components and the mathematical model of some more important neurons and one numerical simulation of the linear neural network In the paper was used the least mean square (LMS) error algorithm for adjusting the weights and biases and incremental training by different training rate, finally to obtain one minimum error to the target

Assisted Research of the Neural Network

The paper shown one assisted method to construct simple and complex neural network and to simulate on-line them. By on-line simulation of some more important neural simple and complex network is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and delay of time. There are shown some important neurons type, transfer functions, weights and biases of neurons, and some complex layers with different type of neurons. By using the proper virtual LabVIEW instrumentation in on-line using, were established some influences of the network parameters to the number of iterations before canceled the mean square error to the target. Numerical simulation used the proper teaching law and proper virtual instrumentation. In the optimization step of the research on used the minimization of the error function between the output and the target.

Optimization of the Neural Network by Using the LabVIEW Instrumentation

Finding the better solution of the neural network design to solve the inverse kinematics problem with the minimum of the trajectory errors is very difficult, because there are many variable parameters and many redundant solutions. The presented paper show the assisted research of the influences of some more important parameters to the final end-effector trajectory errors of the proposed neural network model solving the inverse kinematics problem. We were been studied the number of neurons in each layers, the sensitive function for the first and second layer, the magnifier coefficient of the trajectory error, the variable step of the time delay and the position of this block, the different cases of target data and the case when the hidden target data were adjusted. All obtained results were been verified by applying the proper direct kinematics virtual LabVIEW instrumentation. Finally we were obtained one optimal Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN(TDRL)) type, what can be used to solve the inverse kinematics problem with maximum 4% of trajectory errors.

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Assisted Research and Optimization of the Proper Neural Network Solving the Inverse Kinematics Problem

The actual required productivity, accuracy and reliability impose that the robots must be optimized concerning the dynamic behavior. The joints and robots bodies are necessary to be optimized for their usability performance to assure the productivity requirements. The global dynamic compliance (GDC) is one of the most important dynamic parameters of the dynamic behavior of the industrial robots. The viscose global dynamic damper coefficient (VGDDC) is other important parameter of the dynamic behavior what must be optimized to obtain the desired dynamic behavior, the avoiding of the resonance frequencies. The paper shows one new assisted method of the GDC analyzes of the industrial robot with LabVIEW virtual instrumentation (VI) in three different cases: with/without smart magnetorheological damper (MRD) and with aero damper. The created VI-s assures to obtain the assisted research of the dynamic behavior. With this research was possible to determine in the frequencies domain, the robot GDC and the viscose global dynamic damper equivalent coefficient (VGDDEC) value in a case with MRD and finally the transmission of the vibration from the floor to the robot’s tool center point (TCP). This method and the created virtual LabVIEW instrumentation are generally and they are possible to apply in many others dynamic behavior researches.

Optimizing the Global Dynamic Compliance by Using the Smart Damper and LabVIEW Instrumentation

The paper showed the assisted research of one new model of digital dynamic neural network by using the LabVIEW proper virtual instrumentation and proper mathematical model. In the research were used some different way to optimize the convergence process, for example: using one time- delay of the first and second output from the neural layers; using the recursive link and time- delay; using the bipolar sigmoid hyperbolic tangent sensitive function replacing the sigmoid simple sensitive function. By on-line simulation of the neural network it is possible to know what will be the influences of all network parameters like the input data, weight, biases matrix, sensitive functions, closed loops and time- delay, to the gradient errors, in a convergence process. By on-line using the proper virtual LabVIEW instrumentation, were established some influences of the network parameters: number of input vector data, number of neurons in each layers, to the number of iterations before canceled the mean square error to the target. In the optimization research we used the minimization of the gradient error function between the output and the target.

Dan Paune

Papers

Assisted Research of the Neural Network

Optimization of the Neural Network by Using the LabVIEW Instrumentation

Assisted Research and Optimization of the Proper Neural Network Solving the Inverse Kinematics Problem

Optimizing the Global Dynamic Compliance by Using the Smart Damper and LabVIEW Instrumentation

Assisted Research of the Dynamic Neural Networks with Time-Delays and Recurrent Links