Robotic Arm Control By Fine-Tuned Convolutional Neural Network Model

被引：0

作者：

Bayraktar, Ertugrul ^{[1
]}

Yigit, Cihat Bora ^{[2
]}

Boyraz, Pinar ^{[2
]}

机构：

[1] Istanbul Tech Univ, Fen Bilimleri Enstitusu, Mekatron Muhendisligi, Istanbul, Turkey

[2] Istanbul Tech Univ, Makina Fak, Makina Muhendisligi, Istanbul, Turkey

来源：

2017 25TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2017年

关键词：

deep convolutional neural networks; object recognition; robotics; control; OBJECT;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Obtaining semantic information is crucial in order to implement complex robotic applications successfully. Therefore, it commonly expected from the robotics systems to be equipped with advanced hardware and software. In this study, the simulation results of a robotic arm, which manipulates the recognized objects using deep neural networks considering the physical features, are given for 10 different categories. An accuracy rate of %97.28 is achieved as a result of the fine-tuning of the deep neural network called VGGNet16 by using the dataset which is composed of 1000 training images and 400 testing images in each category. In addition, successful displacement results are obtained for all objects.

引用

页数：4

共 48 条

[41] Using neural network in a model-based predictive control loop to enhance energy performance of buildings [J].

Agouzoul, Abdelali ;

Chegari, Badr ;

Tabaa, Mohamed ;

Simeu, Emmanuel .

ENERGY REPORTS, 2022, 8 :1196-1207

[42] Implementation of neural network inverse-model-based control (NN-IMBC) strategy in batch reactors [J].

Aziz, N ;

Hussain, MA ;

Mujtaba, IM .

PROCESS SYSTEMS ENGINEERING 2003, PTS A AND B, 2003, 15 :708-713

[43] Hot-air tunel control using multi-dimensional predictive controller based on neural network model [J].

Nepevny, P. ;

Pivonka, P. .

Annals of DAAAM for 2006 & Proceedings of the 17th International DAAAM Symposium: INTELLIGENT MANUFACTURING & AUTOMATION: FOCUS ON MECHATRONICS AND ROBOTICS, 2006, :267-268

[44] Uncertainty Aware Model predictive control for free-floating space manipulator based on probabilistic ensembles neural network [J].

Wang, Xu ;

Liu, Yanfang ;

Qi, Ji ;

Qi, Naiming ;

Peng, Na .

ADVANCES IN SPACE RESEARCH, 2024, 74 (10) :5044-5056

[45] Synthesis of industrial robot energy consumption simulation model based on a neural network modules of electric drive control systems [J].

Gorkavyy, Mikhail. A. ;

Melnichenko, Markel A. ;

Voroschenko, Vasilii D. .

MARINE INTELLECTUAL TECHNOLOGIES, 2025, (02)

[46] Hydraulic actuator control with open-centre electrohydraulic valve using a cerebellar model articulation controller neural network algorithm [J].

Pinsopon, U ;

Hwang, T ;

Cetinkunt, S ;

Ingram, R ;

Zhang, Q ;

Cobo, M ;

Koehler, D ;

Ottman, R .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 1999, 213 (I1) :33-48

[47] Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System [J].

Viejo, Claudia Gonzalez ;

Torrico, Damir D. ;

Dunshea, Frank R. ;

Fuentes, Sigfredo .

BEVERAGES, 2019, 5 (02)

[48] Comparison of Physics-Based, Semi-Empirical and Neural Network-Based Models for Model-Based Combustion Control in a 3.0 L Diesel Engine [J].

Hu, Song ;

d'Ambrosio, Stefano ;

Finesso, Roberto ;

Manelli, Andrea ;

Marzano, Mario Rocco ;

Mittica, Antonio ;

Ventura, Loris ;

Wang, Hechun ;

Wang, Yinyan .

ENERGIES, 2019, 12 (18)

← 1 2 3 4 5 →