Performance Analysis of Data-Driven Techniques for Solving Inverse Kinematics Problems

2021 ◽  
pp. 85-99
Author(s):  
Vijay Bhaskar Semwal ◽  
Yash Gupta
Keyword(s):  
Performance Analysis ◽  
Inverse Kinematics ◽  
Data Driven

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.

Data-Driven Geospatial-Enabled Transportation Platform for Freeway Performance Analysis

2015 ◽  
Vol 7 (2) ◽  
pp. 10-21 ◽  
Author(s):  
Sonia Xiao ◽  
Xiaoyue Cathy Liu ◽  
Yinhai Wang
Keyword(s):  
Performance Analysis ◽  
Data Driven

Advanced Data-Driven Performance Analysis For Mature Waterfloods (Russian)

2015 ◽  
Author(s):  
Runo Mijnarends ◽  
Andrey Frolov ◽  
Fedor Grishko ◽  
Svyatoslav Kryanev ◽  
Egor Mikhaylenko ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Data Driven

scholarly journals Data-Driven Shoulder Inverse Kinematics

2016 ◽  
Vol 6 (4) ◽  
pp. 01-13
Author(s):  
YoungBeom Kim ◽  
Byung-Ha Park ◽  
Kwang-Mo Jung ◽  
JungHyun Han
Keyword(s):  
Inverse Kinematics ◽  
Data Driven

Data Driven Inverse Kinematics of Soft Robots using Local Models

2019 ◽  
Author(s):  
Fredrik Holsten ◽  
Morten Pol Engell-Norregard ◽  
Sune Darkner ◽  
Kenny Erleben
Keyword(s):  
Inverse Kinematics ◽  
Data Driven ◽  
Local Models ◽  
Soft Robots

scholarly journals PADDLE: Performance Analysis Using a Data-Driven Learning Environment

2018 ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Rushil Anirudh ◽  
Bhavya Kailkhura ◽  
Nikhil Jain ◽  
Tanzima Islam ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Learning Environment ◽  
Data Driven

A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants

2017 ◽  
Vol 208 ◽  
pp. 1430-1440 ◽  
Author(s):  
Dario Torregrossa ◽  
Joachim Hansen ◽  
Francesc Hernández-Sancho ◽  
Alex Cornelissen ◽  
Georges Schutz ◽  
...  
Keyword(s):  
Waste Water ◽  
Energy Efficiency ◽  
Water Treatment ◽  
Performance Analysis ◽  
Waste Water Treatment ◽  
Data Driven ◽  
Efficiency Optimization ◽  
Pump Performance ◽  
Water Treatment Plants ◽  
Waste Water Treatment Plants

Performance Analysis of Data-Driven Pipelined Computer Architectures

2000 ◽  
Vol 20 (3) ◽  
pp. 236-247
Author(s):  
Kuang-Hwei Chi ◽  
Chien-Chao Tseng ◽  
Chih-Zong Lin ◽  
Wen-Kuang Chou
Keyword(s):  
Performance Analysis ◽  
Computer Architectures ◽  
Data Driven
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