scholarly journals Use of Jacobians for inverse kinematics of articulated robots : a study on approximate solutions

2020 ◽  
Author(s):  
◽  
Uriel Jacket Tresor Demby's
Keyword(s):  
Numerical Solution ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Numerical Solutions ◽  
Fuzzy Inference ◽  
Numerical Approach ◽  
Data Driven ◽  
Approximate Methods ◽  
Inference System ◽  
First Case

In the context of articulated robotic manipulators, the Forward Kinematics (FK) is a highly non-linear function that maps joint configurations of the robot to poses of its endeffector. Furthermore, while in the most useful cases these functions are neither injective (one-to-one) nor surjective (onto), depending on the robot configuration -- i.e. the sequence of prismatic versus revolute joints, and the number of Degrees of Freedom (DoF) -- the associated Inverse Kinematics (IK) problem may be practically or even theoretically impossible to be solved analytically. Therefore, in the past decades, several approximate methods have been developed for many instances of IK problems. The approximate methods can be divided into two distinct categories: data-driven and numerical approaches. In the first case, data-driven approaches have been successfully used for small workspace domains (e.g., task-driven applications), but not fully explored for large ones, i.e. in task-independent applications where a more general IK is required. Similarly, and despite many successful implementations over the years, numerical solutions may fail if an improper matrix inverse is employed (e.g., Moore-Penrose generalized inverse). In this research, we propose a systematic, robust and accurate numerical solution for the IK problem using the Unit-Consistent (UC) and the Mixed (MX) Inverse methods to invert the Jacobians derived from the Denavit-Hartenberg (D-H) representation of the FK for any robot. As we demonstrate, this approach is robust to whether the system is underdetermined (less than 6 DoF) or overdetermined (more than 6 DoF). We compare the proposed numerical solution to data driven solutions using different robots -- with DoF varying from 3 to 7. We conclude that numerical solutions are easier to implement, faster, and more accurate than most data-driven approaches in the literature, specially for large workspaces as in task-independent applications. We particularly compared the proposed numerical approach against two data-driven approaches: Multi-Layer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), while exploring various architectures of these Neural Networks (NN): i.e. number of inputs, number of outputs, depth, and number of nodes in the hidden layers.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

A new meta-heuristic optimization algorithm based on a paradigm from physics: string theory

2021 ◽  
Vol 41 (1) ◽  
pp. 1657-1675
Author(s):  
Luis Rodriguez ◽  
Oscar Castillo ◽  
Mario Garcia ◽  
Jose Soria
Keyword(s):  
String Theory ◽  
Optimization Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Mathematical Functions ◽  
Inference System ◽  
First Case

The main goal of this paper is to outline a new optimization algorithm based on String Theory, which is a relative new area of physics. The String Theory Algorithm (STA) is a nature-inspired meta-heuristic, which is based on studies about a theory stating that all the elemental particles that exist in the universe are strings, and the vibrations of these strings create all particles existing today. The newly proposed algorithm uses equations based on the laws of physics that are stated in String Theory. The main contribution in this proposed method is the new techniques that are devised in order to generate potential solutions in optimization problems, and we are presenting a detailed explanation and the equations involved in the new algorithm in order to solve optimization problems. In this case, we evaluate this new proposed meta-heuristic with three cases. The first case is of 13 traditional benchmark mathematical functions and a comparison with three different meta-heuristics is presented. The three algorithms are: Flower Pollination Algorithm (FPA), Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). The second case is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting a statistical comparison of these results with respect to FA and GWO. In addition, we are presenting a third case, which is the optimization of a fuzzy inference system (FIS), specifically finding the optimal design of a fuzzy controller, where the main goal is to optimize the membership functions of the FIS. It is important to mention that we used these study cases in order to analyze the proposed meta-heuristic with: basic problems, complex problems and control problems. Finally, we present the performance, results and conclusions of the new proposed meta-heuristic.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot 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 ANFIS gives better result and minimum error as compared to ANN.</span>

scholarly journals A novel numerical approach for fracture analysis in orthotropic media

2017 ◽  
Vol 20 (K2) ◽  
pp. 5-13
Author(s):  
Minh Ngoc Nguyen ◽  
Nha Thanh Nguyen ◽  
Tinh Quoc Bui ◽  
Thien Tich Truong
Keyword(s):  
Degrees Of Freedom ◽  
Numerical Solutions ◽  
Numerical Approach ◽  
Fracture Analysis ◽  
Orthotropic Materials ◽  
Displacement Fields ◽  
Novel Approach ◽  
Interpolation Procedure ◽  
Orthotropic Media ◽  
Standard Finite Element Method

This paper presents a novel approach for fracture analysis in two-dimensional orthotropic domain. The proposed method is based on consecutive-interpolation procedure (CIP) and enrichment functions. The CIP were recently introduced as an improvement for standard Finite Element method, such that higher-accurate and higher-continuous solution can be obtained without smoothing operation and without increasing the number of degrees of freedom. To avoid re-meshing, the enrichment functions are employed to mathematically describe the jump in displacement fields and the singularity of stress near crack tip. The accuracy of the method for analysis of cracked body made of orthotropic materials is studied. For that purpose, various examples with different geometries and boundary conditions are considered. The results of stress intensity factors, a key quantity in fracture analysis, are validated by comparing with analytical solutions and numerical solutions available in literatures.

New continuum surgical robot based on hybrid concentric tube-tendon driven mechanism

2021 ◽  
pp. 095440622110424
Author(s):  
Mohammed Abdel-Nasser ◽  
Omar Salah
Keyword(s):  
Inverse Kinematics ◽  
High Performance ◽  
Analytical Study ◽  
Fuzzy Inference ◽  
Closed Form Solution ◽  
Form Solution ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Intelligent Approach

Robotics technology is used widely in minimally invasive surgery (MIS) which provides high performance and accuracy. The most famous robot arm mechanisms, which are used in MIS, are tendon-driven mechanism (TDM), and concentric tube mechanism (CTM). Unfortunately, these mechanisms until now have some limitations, i.e. making friction with the tissue during extracting and retracting and strain limits, for TDM and CTM respectively. A new hybrid concentric tube-tendon driven mechanism (HCTDM) is proposed to overcome these limitations. HCTDM enables the end-effector to get close to and get away from the surgical area during the operation without harming the tissue and with more flexibility. In addition to that, the workspace increases as a result of this combination, too. This benefit serves MIS, especially endoscopic surgeries (ESs). We did an analytical study of this idea and got the forward kinematics. In the inverse kinematics, an intelligent approach which is called an adaptive neuro-fuzzy inference system (ANFIS) is used because the closed-form solution is more complicated for such these mechanisms. Finally, HCTDM is analyzed and evaluated by using a computer simulation. The simulation results show that the workspace becomes wider and has more dexterity than use TDM or CTM individually. Furthermore, various trajectories are used to test the mechanism and the kinematic analysis, which show the mechanism can follow and track the trajectories with maximum mean error 1.279, 0.7027, and [Formula: see text] for X, Y, and Z axes respectively.

Adaptive neuro-fuzzy inference system–based path planning of 5-degrees-of-freedom spatial manipulator for medical applications

2018 ◽  
Vol 232 (7) ◽  
pp. 726-732 ◽  
Author(s):  
Jyotindra Narayan ◽  
Ekta Singla ◽  
Sanjeev Soni ◽  
Ashish Singla
Keyword(s):  
Path Planning ◽  
Closed Form ◽  
Fuzzy Inference System ◽  
Degrees Of Freedom ◽  
Fuzzy Inference ◽  
Inverse Kinematic ◽  
Inference System ◽  
Closed Form Solutions ◽  
Neuro Fuzzy ◽  
Patient Side

Over the last few decades, medical-assisted robots have been considered by many researchers, within the research domain of robotics. In this article, a 5-degrees-of-freedom spatial medical manipulator is analyzed for path planning, based on inverse kinematic solutions. Analytical methods have generally employed for finding the inverse kinematic solutions in earlier studies. However, this method is only appreciable in case of closed-form solutions. The unusual joint configurations of considered manipulator result in more complexity to attain the closed-form solutions, analytically. To overcome with shortcomings of analytical method, a non-traditional approach named adaptive neuro-fuzzy inference system is proposed under the class of artificial intelligent techniques. This article presents this neuro-fuzzy approach for desired path generation by 5-degrees-of-freedom manipulator. The estimation of percentage error between actual path and adaptive neuro-fuzzy inference system–generated path is done with respect to x, y, and z directions, respectively. Furthermore, the error between actual and predicted values regarding joint parameters is calculated for a certain arm matrix. The prototype of 5-degrees-of-freedom medical-assisted manipulator is developed at CSIR-CSIO Laboratory Chandigarh, which is also termed as patient-side manipulator to be utilized in robot-assisted surgery. Through the simulation runs, in this work, it is found that the results from adaptive neuro-fuzzy inference system approach are quite satisfactory and acceptable.

scholarly journals Evaluation of data-driven models to downscale rainfall parameters from global climate models outputs: the case study of Latyan watershed

2018 ◽  
Vol 11 (1) ◽  
pp. 200-216 ◽  
Author(s):  
Reza Haji Hosseini ◽  
Saeed Golian ◽  
Jafar Yazdi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Models ◽  
Fuzzy Inference ◽  
Global Climate ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Data Driven ◽  
Observation Data ◽  
Inference System

Abstract Assessment of climate change in future periods is considered necessary, especially with regard to probable changes to water resources. One of the methods for estimating climate change is the use of the simulation outputs of general circulation models (GCMs). However, due to the low resolution of these models, they are not applicable to regional and local studies and downscaling methods should be applied. The purpose of the present study was to use GCM models' outputs for downscaling precipitation measurements at Amameh station in Latyan dam basin. For this purpose, the observation data from the Amameh station during the 1980–2005 period, 26 output variables from two GCM models, namely, HadCM3 and CanESM2 were used. Downscaling was performed by three data-driven methods, namely, artificial neural network (ANN), nonparametric K-nearest neighborhood (KNN) method, and adaptive network-based fuzzy inference system method (ANFIS). Comparison of the monthly results showed the superiority of KNN compared to the other two methods in simulating precipitation. However, all three, ANN, KNN, and ANFIS methods, showed satisfactory results for both HadDCM3 and CanESM2 GCM models in downscaling precipitation in the study area.

Structure design and kinematics of a robot manipulator

Robotica ◽  
1988 ◽  
Vol 6 (4) ◽  
pp. 299-309 ◽  
Author(s):  
Kesheng Wang ◽  
Terje K. Lien
Keyword(s):  
Numerical Solution ◽  
Closed Form ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Closed Form Solution ◽  
Form Solution ◽  
Structure Design ◽  
General Algorithm ◽  
6 Degrees Of Freedom

SUMMARYIn this paper we show that a robot manipulator with 6 degrees of freedom can be separated into two parts: arm with the first three joints for major positioning and wrist with the last three joints for major orienting. We propose 5 arms and 2 wrists as basic construction for commercially robot manipulators. This kind of simplification can lead to a general algorithm of inverse kinematics for the corresponding configuration of different combinations of arm and wrist. The approaches for numerical solution and closed form solution presented in this paper are very efficient and easy for calculating the inverse kinematics of robot manipulator.

scholarly journals Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1383
Author(s):  
Eunjeong Lee ◽  
Taegeun Kim
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Fuzzy Inference ◽  
Oxygen Demand ◽  
Data Driven ◽  
Anfis Model ◽  
Hydrological Simulation ◽  
Inference System ◽  
Hydrological Conditions ◽  
Irrigation Period

The water quality of the Dongjin River deteriorates during the irrigation period because the supply of river maintenance water to the main river is cut off by the mass intake of agricultural weirs located in the midstream regions. A physics-based model and a data-driven model were used to predict the water quality in the Dongjin River under various hydrological conditions. The Hydrological Simulation Program–Fortran (HSPF), which is a physics-based model, was constructed to simulate the biological oxygen demand (BOD) in the Dongjin River Basin. A Gamma Test was used to derive the optimal combinations of the observed variables, including external water inflow, water intake, rainfall, and flow rate, for irrigation and non-irrigation periods. A data-driven adaptive neuro-fuzzy inference system (ANFIS) model was then built using these results. The ANFIS model built in this study was capable of predicting the BOD from the observed hydrological data in the irrigation and non-irrigation periods, without running the physics-based model. The predicted results have high confidence levels when compared with the observed data. Thus, the proposed method can be used for the reliable and rapid prediction of water quality using only monitoring data as input.

scholarly journals Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

2020 ◽  
Vol 48 (12) ◽  
pp. 2976-2987
Author(s):  
Abdul Haleem Butt ◽  
Erika Rovini ◽  
Hamido Fujita ◽  
Carlo Maremmani ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fuzzy Inference ◽  
Inertial Sensors ◽  
Objective Assessment ◽  
Data Driven ◽  
Inference System ◽  
Regression Algorithms ◽  
Severity Levels ◽  
Wearable Inertial Sensors

AbstractParkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

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