FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)

<p align="justify">In this research, Fusion sensor and Kalman Filter method will be implemented with input from two sensors are IMU (Inertial Measurement Unit) and GPS (Global Positioning System). This method is applied with the assumption that the system used will correct the error...

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Main Author: RIDWAN AS'AD - NIM : 23215054 , MUHAMMAD
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/29322
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:29322
spelling id-itb.:293222018-03-02T10:05:24ZFUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things) RIDWAN AS'AD - NIM : 23215054 , MUHAMMAD Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/29322 <p align="justify">In this research, Fusion sensor and Kalman Filter method will be implemented with input from two sensors are IMU (Inertial Measurement Unit) and GPS (Global Positioning System). This method is applied with the assumption that the system used will correct the error of the GPS input data with the help of IMU sensors to get a more accurate result of object position and have minimal error than just using GPS sensor only. The data generated by the sensor will be sent to the server using the MQTT (Message Queuing Telemetry Transport) protocol with the purpose of data accessible anywhere, anytime and by anyone who will retrieve the data. The results of this study indicate that the design of estimation method with fusion sensor and kalman filter is in accordance with expectations or ground truth based. By comparing the sensor output values with other comparable data used as ground truths such as measuring tape, RTK (Real Time Kinematic) and GPS Handled Garmin Oregon 650. To make sure that the output from the fusion kalman filter are satisfied, the range used varies to ensure the result are different from 0 meter until 100 meter. And the results are satisfied enough. And implements this system with a pioneer robot as an actuator of the received sensor value. With the aim of moving the robot to follow the object behind it. The RMSE value obtained from the results of this study is 9.7&#119909;10’( meters, 9.7&#119909;10’( meters, 1.5 meters for measuring tape, RTK and Garmin Oregon 650. And produce a very small RMSE value with the position comparison value of the following robot 3.4&#119909;10’+ meter latitude, 2.9&#119909;10’+ meter longitude for advanced scheme and 0.00002 meter latitude, 2.7&#119909;10’+ meter longitude for turning scheme.<p align="justify"> text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description <p align="justify">In this research, Fusion sensor and Kalman Filter method will be implemented with input from two sensors are IMU (Inertial Measurement Unit) and GPS (Global Positioning System). This method is applied with the assumption that the system used will correct the error of the GPS input data with the help of IMU sensors to get a more accurate result of object position and have minimal error than just using GPS sensor only. The data generated by the sensor will be sent to the server using the MQTT (Message Queuing Telemetry Transport) protocol with the purpose of data accessible anywhere, anytime and by anyone who will retrieve the data. The results of this study indicate that the design of estimation method with fusion sensor and kalman filter is in accordance with expectations or ground truth based. By comparing the sensor output values with other comparable data used as ground truths such as measuring tape, RTK (Real Time Kinematic) and GPS Handled Garmin Oregon 650. To make sure that the output from the fusion kalman filter are satisfied, the range used varies to ensure the result are different from 0 meter until 100 meter. And the results are satisfied enough. And implements this system with a pioneer robot as an actuator of the received sensor value. With the aim of moving the robot to follow the object behind it. The RMSE value obtained from the results of this study is 9.7&#119909;10’( meters, 9.7&#119909;10’( meters, 1.5 meters for measuring tape, RTK and Garmin Oregon 650. And produce a very small RMSE value with the position comparison value of the following robot 3.4&#119909;10’+ meter latitude, 2.9&#119909;10’+ meter longitude for advanced scheme and 0.00002 meter latitude, 2.7&#119909;10’+ meter longitude for turning scheme.<p align="justify">
format Theses
author RIDWAN AS'AD - NIM : 23215054 , MUHAMMAD
spellingShingle RIDWAN AS'AD - NIM : 23215054 , MUHAMMAD
FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)
author_facet RIDWAN AS'AD - NIM : 23215054 , MUHAMMAD
author_sort RIDWAN AS'AD - NIM : 23215054 , MUHAMMAD
title FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)
title_short FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)
title_full FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)
title_fullStr FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)
title_full_unstemmed FUSION SENSOR INS (Inertial Navigation System) AND GPS (Global Positioning System) FOR HUMAN FOLLOWING ROBOT BASED ON IoT (Internet of Things)
title_sort fusion sensor ins (inertial navigation system) and gps (global positioning system) for human following robot based on iot (internet of things)
url https://digilib.itb.ac.id/gdl/view/29322
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