An unsupervised learning based neural network approach for a robotic manipulator

被引：5

作者：

Mahajan A. ^{[1
]}

Singh H.P. ^{[2
]}

Sukavanam N. ^{[3
]}

机构：

[1] Department of Electronics, Sri Venkateswara College, Delhi University, Delhi

[2] Cluster Innovation Centre, Delhi University, Delhi

[3] Department of Mathematics, Indian Institute of Technology, Roorkee

来源：

International Journal of Information Technology | 2017年 / 9卷 / 1期

关键词：

Artificial neural network; Kinematics; Robotic ball catching; Robotic manipulator; Trajectory tracking;

D O I：

10.1007/s41870-017-0002-2

中图分类号：

学科分类号：

摘要：

This paper presents a neural network approach for solving the inverse kinematics of a robotic manipulator. Inverse kinematics equations are more challenging than the forward kinematics equations and therefore are more computationally complex to solve. Here, we are using a neural network approach due to its ability to give more accurate results in complex situations as compared to the other approaches. Moreover, we are using this model for trajectory tracking of a two DOF robotic arm to test its validity in real life situations. © 2017, Bharati Vidyapeeth's Institute of Computer Applications and Management.

引用

页码：1 / 6

页数：5

共 50 条

[21] Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator
Baressi Segota, Sandi
Andelic, Nikola
Car, Zlatan
Sercer, Mario
[J]. TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2021, 28 (04): : 1380 - 1387
[22] Constant Force PID Control for Robotic Manipulator Based on Fuzzy Neural Network Algorithm
Zhu Dachang
Du Baolin
Zhu Puchen
Chen Shouyan
[J]. COMPLEXITY, 2020, 2020
[23] Neural network-based model for classification of faults during operation of a robotic manipulator
Baressi Šegota, Sandi
Anđelić, Nikola
Car, Zlatan
Šercer, Mario
[J]. Tehnicki Vjesnik, 2021, 28 (04): : 1380 - 1387
[24] A Reinforcement-Learning Approach to Control Robotic Manipulator Based on Improved DDPG
Majumder, Saikat
Sahoo, Soumya Ranjan
[J]. 2023 NINTH INDIAN CONTROL CONFERENCE, ICC, 2023, : 281 - 286
[25] Adaptive bias RBF neural network control for a robotic manipulator
Liu, Qiong
Li, Dongyu
Ge, Shuzhi Sam
Ji, Ruihang
Ouyang, Zhong
Tee, Keng Peng
[J]. NEUROCOMPUTING, 2021, 447 : 213 - 223
[26] Neural Network Structures for Identification of Nonlinear Dynamic Robotic Manipulator
Minh Nguyen Duc
Thang Nguyen Trong
[J]. 2014 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (IEEE ICMA 2014), 2014, : 1575 - 1580
[27] Adaptive Neural Network Control for a Robotic Manipulator with Unknown Deadzone
Ge, Shuzhi Sam
He, Wei
Xiao, Shengtao
[J]. 2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 2997 - 3002
[28] A neural network sliding mode controller with application to robotic manipulator
Peng, Jinzhu
Wang, Yaonan
Sun, Wei
Liu, Yan
[J]. WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 2101 - +
[29] The estimation of dynamic parameters of a robotic manipulator by neural network method
Sharan, AM
Aggarwal, R
[J]. SIXTH, SEVENTH, AND EIGHTH WORKSHOPS ON VIRTUAL INTELLIGENCE: ACADEMIC/INDUSTRIAL/NASA/DEFENSE TECHNICAL INTERCHANGE AND TUTORIALS, 1996, 2878 : 171 - 203
[30] Convolutional Neural Network-Based Speckle Tracking for Ultrasound Strain Elastography: An Unsupervised Learning Approach
Wen, Shuojie
Peng, Bo
Wei, Xingyue
Luo, Jianwen
Jiang, Jingfeng
[J]. IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL, 2023, 70 (05) : 354 - 367

← 1 2 3 4 5 →