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Paper Abstract and Keywords
Presentation 2023-12-08 10:50
Mobile Robot Navigation System Implementation Using Extended Kalman Filter with Fusion Sensor
Adam Mail, Mochammad Sahal, Ari Santoso (ITS) SANE2023-79
Abstract (in Japanese) (See Japanese page) 
(in English) Navigation systems play an important role in the development of robot automation where many researchers, the industrial world, and even academic institutions take their roles in the development of navigation systems. This system has the main goal of getting the robot's position precisely and quickly by using the sensor calculations used. In its application, the navigation system is divided into four stages, namely sensor data, perception, planning, and control. Data for each sensor used will be read and collected. This data will be used as the robot's perception. The planning stage uses perceptual information for planning further actions. The results of the planning will be forwarded to be used as control for the robot driving components. In this study, the implementation was carried out by using a mobile robot with mechanical wheels. The integration of the rotary encoder, gyroscope, and lidar sensors in this study used the Kalman filter. Two rotary encoder sensors with a gyroscope are integrated to produce the global position of the mobile robot to the field with the output of the x axis of translation, the axis of translation of y, and the angle of φ. These results will be used as the state of the prediction stage in the Kalman filter. The lidar sensor will be input into the system in the form of a field reference point cloud map and lidar point cloud data. Field reference map point cloud data and lidar point cloud data will be compared using the Iterative Closest Point (ICP) algorithm. Furthermore, the lidar data will be interpolated to produce more sampling. The results obtained from the implementation of the rotary encoder, gyroscope, and lidar sensors were good results where the average yaw angle of rest error was 0.02° on the gyroscope, 0.19° on the ICP, and 0.05° on the EKF. The results also show that the average EKF error can overcome the accumulated errors generated by the rotary encoder and gyroscope sensors.
Keyword (in Japanese) (See Japanese page) 
(in English) ICP / Kalman Filter / Lidar / Mobile Robot / Sensor Fusion / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 298, SANE2023-79, pp. 104-109, Dec. 2023.
Paper # SANE2023-79 
Date of Issue 2023-11-30 (SANE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SANE  
Conference Date 2023-12-07 - 2023-12-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Surakarta, Indonesia 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ICSANE 
Paper Information
Registration To SANE 
Conference Code 2023-12-SANE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Mobile Robot Navigation System Implementation Using Extended Kalman Filter with Fusion Sensor 
Sub Title (in English)  
Keyword(1) ICP  
Keyword(2) Kalman Filter  
Keyword(3) Lidar  
Keyword(4) Mobile Robot  
Keyword(5) Sensor Fusion  
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Keyword(7)  
Keyword(8)  
1st Author's Name Adam Mail  
1st Author's Affiliation Institut Teknologi Sepuluh Nopember (ITS)
2nd Author's Name Mochammad Sahal  
2nd Author's Affiliation Institut Teknologi Sepuluh Nopember (ITS)
3rd Author's Name Ari Santoso  
3rd Author's Affiliation Institut Teknologi Sepuluh Nopember (ITS)
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Speaker Author-1 
Date Time 2023-12-08 10:50:00 
Presentation Time 20 minutes 
Registration for SANE 
Paper # SANE2023-79 
Volume (vol) vol.123 
Number (no) no.298 
Page pp.104-109 
#Pages 6 
Date of Issue 2023-11-30 (SANE) 

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