SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 10(4); March (2024), pp. 304–307
ISSN (Online): 2352-6386; ISSN (Print): 2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Engineering of Autonomous Mobile Field Robot-Validating Precision in Self-Localization via SimulationTakamasa Hayashi1, Shintaro Ogawa1, Yuto Okawachi1, Tan Chi Jie1, Janthori Titan1, Ayumu Tominaga2, Eiji Hayashi1,
Satoko Seino3

Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-city, Fukuoka, 820-8502, Japan
Department of Creative Engineering Robotics and Mechatronics Course, National Institute of Technology, Kitakyushu College, 5-20-1, Kokuraminamiku,
Kitakyushu Shii, Fukuoka, 802-0985, Japan
3
Graduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan
1
2

ARTICLE INFO
Article History
Received 30 November 2023
Accepted 13 April 2024
Keywords
Mobile field robot
Self-localization
Simulation
Extended Kalman Filter (EKF)

ABSTRACTIn recent times, the escalation of marine debris has emerged as a formidable challenge,
particularly in its retrieval. Among this, coastal debris, which falls under the broader category ofmarine debris, can indeed be manually picked up, yet the diversity in its forms and sizes limits
the efficiency of solely human retrieval efforts. In response to this issue, my research concentrated
on the development of an autonomous mobile robot. This study zeroes in on the critical task ofself-localization, a fundamental requirement for autonomous navigation. Within our simulation,
we meticulously recreated a real-world coastal cleanup site and assessed the self-localization
precision utilizing an Extended Kalman Filter (EKF) alongside various sensors.
© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction

2. Autonomous Mobile Field Robot “BUNKER”

In recent years, the amount of marine debris has
increased, posing a challenge in terms of its recovery.
Coastal drift debris, a type of marine debris, can bepicked up by human hands; however, due to the diverseshapes, types, and sizes of the debris, there are limitations
to recovery efforts relying solely on human labor.
Nevertheless, entrusting the collection of debris entirely
to robots presents technical difficulties and incurs
significant costs. Therefore, we focused on engineering
an autonomous mobile field robot capable of collecting
coastal drift debris gathered by humans. In developing therobot, we first created a simulation environment tofacilitate smooth progress in the development process. In
this study, we concentrated on self-localization, which is
crucial for autonomous movement, by replicating an
actual beach cleaning site within the simulation
environment and evaluating the accuracy of selflocalization.

In our earlier research, we engineered an autonomous
mobile field robot [1], utilizing the Kawasaki Heavy
Industries' KFX®90, a gasoline-powered all-terrain
vehicle (ATV), as its base. Nonetheless, the originalvehicle's design imposed limitations, including a wide
turning radius and challenges in stable maneuverability
across steep inclines and rugged terrains, among others.
Addressing these issues led to a platform transition. The
updated platform incorporates Agilex's BUNKER [2] , asdepicted in Fig. 1. Key modifications include a dualopposite wheel travel mechanism and crawler drive
wheels, significantly enhancing its ability to execute
extremely tight turns and surpass the maneuverability of
traditional platforms. Moreover, the robot is now capableof ascending hills with a 36° incline, potentially allowing
for greater speeds. For autonomous navigation, the robot
is outfitted with an RGB-D sensor and 3D LiDAR within
its visual system, and it houses an encoder in the vehicle's
body to track wheel rotation speeds.

Corresponding author E-mail: ookawachi.yuuto693@mail.kyutech.jp, tominaga@kct.ac.jp, haya@mse.kyutech.ac.jp, seino@civil.kyushu-u.ac.jp
URL: http://www.kyutech.ac.jp/304

a. KFX®90
b. BUNKER
Fig. 1. Platform Appearance

Fig. 3. Data fed into and retrieved from EKF
3. Experiment2.1. Simulation Environment Construction

In the coastal settings illustrated in Fig. 4 and Fig. 5, we
designated the robot's initial location as the coordinate
system's origin, setting three target points at coordinates(40, -5), (40, 0), and (40, 5). The robot was remotely
guided to each of these points five times to gather
essential odometry data. For the purpose of selflocalization, we applied both singular odometry
techniques and a combination of multiple odometries
through the Extended Kalman Filter (EKF), leading to a
total of seven distinct estimation approaches. To computethe accuracy of these estimations, we employed the Mean
Absolute Error (MAE) metric.

In our research, we successfully simulated the Agilex
BUNKER within a virtual environment, alongside an
accurate reproduction of Hokuto Mizukumi Park'scoastline in Munakata City, Fukuoka Prefecture, a site ofactual beach cleanup efforts. This was achieved using
advanced 3D mapping technologies from previous
studies, which utilized drones. For the simulation
platform, we chose Gazebo, a robust 3D dynamic
simulator that specializes in the realistic and precise
simulation of robot groups in varied settings, both indoor
and outdoor. Furthermore, Gazebo offers seamlessintegration with the Robot Operating System (ROS) [3].
Illustrations of the simulated BUNKER and the coastal
area on Gazebo are presented in Fig. 2.

a. BUNKER
b. Coast
Fig. 2. Constructed simulation environment
Fig. 4. Testing setup

2.2. Self-Localization System Utilizing EKF
The measurement of a mobile robot's posture isconducted using a range of sensors and techniques.
Nonetheless, the data collected from these sensors is notexact and is believed to include discrepancies. In thisresearch, we utilized the Extended Kalman Filter (EKF),
as depicted in Fig. 3, for the integration of odometry datato ensure a sturdy and minimal-error self-localization
process. Our system incorporated wheel odometry, RGBD odometry, and LiDAR odometry, either singularly orin amalgamation, to generate a consolidated odometry
output.

a. Seashore cross-sectional viewb. Perspective from starting point
Fig. 5. Experiment Environment
3.1. Result
Table 1 presents a summary of the self-localization
errors along the x-axis. The most accurate results wereobserved in Pattern 4, where the integration of wheel
odometry and RGB-D odometry through the Extended
Kalman Filter (EKF) resulted in the lowest error rates for
self-localization. Moreover, an analysis of Pattern 2305

reveals that using only RGB-D odometry yieldedcomparably effective values. However, in Pattern 3, it
was noted that the LiDAR-based approach experienced
an error margin of approximately 20 meters, suggesting
its limited utility in the beach simulation environmentthat mimics a real coastal setting.

It is also necessary to set the friction coefficient toreproduce a more realistic environment in the simulation.
4. Conclusion
In our research, we initially crafted a simulation
environment for crafting an autonomous field robot.
Within this environment, we evaluated the precision ofself-localization techniques. Our findings demonstrated
that a combined odometry approach, merging wheel
odometry with RGB-D odometry via RGB-D sensors andthe Extended Kalman Filter (EKF), facilitated moreaccurate self-positioning. Conversely, LiDAR-based
odometry was found to be less effective in coastal
scenarios characterized by a lack of distinct landmarks,
suchasthoseexaminedinourstudy.
Looking ahead, our strategy includes the integration ofan Inertial Measurement Unit (IMU) to further refine
self-localization accuracy. Beyond just improving selflocalization, we're set to incrementally introduce map
creation and route planning capabilities, crucial for
autonomous movement. In particular, by predefining
waterfronts and rocky areas as non-navigable zones, webelieve that efficient route generation can be achieved.
Additionally, we aim to extend our testing to encomp ass
more demanding settings, like sandy beaches, z towards
integrating these advancements with actual robot systems.

Table 1. X-axis Self-Localization Error Analysis[m]Table 2 outlines the discrepancies found in the selflocalization accuracy along the y-axis. These
discrepancies were markedly larger than those observedin the x-axis direction, indicating a broader variance inaccuracy across all patterns. A consistent high level ofself-localization precision was not realized in any of thepatterns. Although certain input destinations produced
precise outcomes, there was no stable pattern of accuracy.
Within these variations, the odometry method combining
wheel odometry with RGB-D odometry through theExtended Kalman Filter (EKF) recorded the minimalerror.

References
1.

Table 2. Y-axis Self-Localization Error Analysis[m]2.
3.

Ayumu Tominaga, Akihiro Koubara, Ryusuke Fujisawa,
Eiji Hayashi, Abbe Mowshowitz. Development of lidar
based navigation system for automation of tree harvestingprocess, Proceedings of International Conference on
Artificial Life and Robotics 2021, pp. 469-471
BUNKER-Agilex Robotics
ROS, Open Robotics

Authors Introduction
Mr. Takamasa Hayashi
He received his Bachelor’s degree inEngineering in 2023 from intelligent
and Control Systems, Kyushu Instituteof technology in Japan. He is currently
a Master student in Kyushu Institute ofTechnology and conducts research at
Hayashi Laboratory.

3.2. Consideration
In this study, it was observed that RGB-D odometry
exhibited some errors but significantly enhanced theprecision of self-localization. Conversely, LiDAR
odometry's accuracy was notably poor in coastal settings
such as those examined here, primarily due to the absenceof clear landmarks for orientation. The experimental
setup included a slope that declined towards the right of
the travel path, as shown in Fig. 5, causing the vehicle toexperience lateral slippage that predominantly impacted
the accuracy of self-localization on the y-axis. To address
this issue, we suggest the incorporation of an Inertial
Measurement Unit (IMU) to improve the determinationof the vehicle's posture. Previous studies have indicatedthe IMU's considerable potential in improving accuracy.

Mr. Shintaro Ogawa
He received his Bachelor’s degree inEngineering in 2022 from intelligent
and Control Systems, Kyushu Instituteof technology in Japan. He is currently
a Master student in Kyushu Institute ofTechnology and conducts research at
Hayashi Laboratory.

306

Mr. Yuto Okawachi
He received his Bachelor’s degree inEngineering in 2023 from intelligentand Control Systems, Kyushu Instituteof technology in Japan. He is currently
a Master student in Kyushu Institute ofTechnology and conducts research at
Hayashi Laboratory.

Mr. Tan Chi Jie

Dr. Satoko Seino

He received his Master of Creative
Informatics from the Department ofComputer Science and Systems
Engineering, Kyushu Institute ofTechnology, Japan in 2023. He iscurrently a Doctoral student at Kyushu
Institute of Technology and conducts
research at Hayashi Laboratory.

Mr. Janthori TitanHe received his Bachelor ofEngineering in Robotic Engineeringand Automation System from theDepartmentofProduction
Engineering, Faculty of Engineering,
King Mongkut’s University ofTechnology North Bangkok in 2022.
He is currently a Master student at
Kyushu Institute of Technology and conducts research at
Hayashi Laboratory.
Dr. Ayumu Tominaga
He is a assistant professor in
Department of Creative Engineering
Robotics and Mechatronics Course at
National Institute of Technology
Kitakyushu College. He received Dr.
Eng. degree from Kyushu Institute ofTechnology in 2021. His researchinterests
includeIntelligent
mechanics, Mechanical systems and Perceptual
information processing.
Dr. Eiji Hayashi

He is a professor in the Department ofIntelligent and Control Systems atKyushu Institute of Technology. He
received the Ph.D. (Dr. Eng.) degreefrom Waseda University in 1996. His
research interests include Intelligent
mechanics, Mechanical systems andPercentual information processing. He
is a member of IEEE and JSME.

307

She is an associate professor inGraduate School of Engineering,
Kyushu university. She received the(Eng.)
fromKyushu
Ph.D.
University. Her research interests
includecoastalandriverine
environmental conservation.