Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model

Author(s):  
Aleksandar Jokić ◽  
Milica Petrović ◽  
Zbigniew Kulesza ◽  
Zoran Miljković
Author(s):  
Milica Petrovic ◽  
Arkadiusz Mystkowski ◽  
Aleksandar Jokic ◽  
Lazar Dokic ◽  
Zoran Miljkovic

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Basuki Rahmat ◽  
Budi Nugroho

The paper presents the intelligent surveillance robotic control techniques via web and mobile via an Internet of Things (IoT) connection. The robot is equipped with a Kinect Xbox 360 camera and a Deep Learning algorithm for recognizing objects in front of it. The Deep Learning algorithm used is OpenCV's Deep Neural Network (DNN). The intelligent surveillance robot in this study was named BNU 4.0. The brain controlling this robot is the NodeMCU V3 microcontroller. Electronic board based on the ESP8266 chip. With this chip, NodeMCU V3 can connect to the cloud Internet of Things (IoT). Cloud IoT used in this research is cloudmqtt (https://www.cloudmqtt.com). With the Arduino program embedded in the NodeMCU V3 microcontroller, it can then run the robot control program via web and mobile. The mobile robot control program uses the Android MQTT IoT Application Panel.


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