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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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/29322 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | <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 9.7𝑥10’( meters, 9.7𝑥10’( 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 3.4𝑥10’+ meter latitude, 2.9𝑥10’+ meter longitude for advanced scheme and 0.00002 meter latitude, 2.7𝑥10’+ meter longitude for turning scheme.<p align="justify"> |
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