Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

Kazuki Zushida; Zhang Haohao; Hideaki Shimamura; Kazuhiro Motegi; Yoichi Shiraishi

doi:doi:10.11648/j.ijmea.20210901.12

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Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

Kazuki Zushida, Zhang Haohao, Hideaki Shimamura, Kazuhiro Motegi, Yoichi Shiraishi

Published in International Journal of Mechanical Engineering and Applications (Volume 9, Issue 1)

Received: 14 January 2021 Accepted: 21 January 2021 Published: 23 February 2021

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Abstract

This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.

Published in	International Journal of Mechanical Engineering and Applications (Volume 9, Issue 1)
DOI	10.11648/j.ijmea.20210901.12
Page(s)	6-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Random Forest, Robotic Lawn Mower, Lawn Grass Length Estimation, Machine Learning, Hybrid Twin, Sensor Fusion

References

[1]	Kazuki Zushida, Zhang Haohao, Hideaki Shimamura, Kazuhiro Motegi and Yoichi Shiraishi, “Estimation of Lawn Grass Lengths based on Random Forest Algorithm for Robotic Lawn Mower,” Proceedings of the SICE Annual Conference 2020, pp. 1628-1633. September 2020.
[2]	Masaki Kojima, Hideaki Shimamura, Kazuhiro Motegi and Yoichi Shiraishi, “Comparison of Shallow Neural Network with Random Forest Algorithm in Estimating Lawn Grass Lengths for Robotic Lawn Mowers,” Proceedings of the SICE Annual Conference 2020, pp. 1634-1639. September 2020.
[3]	Kazuki Ootake, Hideaki Shimamura, Kazuhiro Motegi and Yoichi Shiraishi, “A Travelling Simulator for Robotic Lawn Mowers based on Particle Filter Approach,” Proceedings of the SICE Annual Conference 2020, pp. 1640-1645. September 2020.
[4]	Francisco Chinesta, Elias Cueto, Emmanuelle Abisset-Chavanne and J. L. D. Virtual, “Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data,” Archives of Computational Methods in Engineering, DOI: 10.1007/s11831-018-9301-4, 2018.
[5]	Abdulmotaleb El Saddik. “Digital Twins: The Convergence of Multimedia Technologies,” IEEE Multimedia, vol. 25, no. 2, pp. 87-92, 2018.
[6]	Bahador Khaleghi, Alaa Khamis, Fakhreddine O. Karrya, and Saiedeh N. Razavi, "Multisensor data fusion: A review of the state-of-the-art," Information Fusion, 14 pp. 28-44, 2013.
[7]	Furqan Alam, Rashid Mehmood, Iyad Katib, Nasser N. Albogam, and Aiiad Alebeshr, "Data Fusion and IoT for Smart Ubiquitous Environments: A Survey," Special Section on Trends and Advances for Ambient Intelligence with Internet of Things (IoT) Systems, Vol. 5, pp. 9533-9554, 2017.
[8]	Go Takami, Moe Tokuoka, Hirotsugu Goto and Yuuichi Nozaka, "Machine Learning Applied to Sensor Data Analysis," Yokogawa Technical Report English Edition, Vol. 59, No. 1, pp. 27-30, 2016.
[9]	Serafin Alonso, Daniel Perez, Antonio Moran, Juna Jose Fuertes, Ignacio Diaz and Manuel Domingues, "A Deep Learning Approach for Fusing Sensor Data from Scre Compressors," Sensors 2019, 19 (13), 2868; https://doi.org/10.3390/s19132868 - 28 Jun 2019.
[10]	Chuan Li, Rene-Vinicio Sanchez, Grover Zurita, Mariela Corrada and Diego Cabrera, "Fault Diagnosis for Rotating Machinery Uring Vibration Measurement Deep Statistical Feature Learning," Sensors 2016, 16, 895; doi: 10.3390/s16060895.
[11]	Toshiaki Kawakami, Hiroshi Kobayashi, Makoto Yamamura, Shuhei Maruyama: “Development of Robotic Lawnmower Miimo,” Honda R&D Technical Review, Vol. 25, No. 2, pp. 54-59, September 2013, https://www.hondarandd.jp/point.php?pid=968&lang=en.
[12]	Leo Breiman, “Random Forests,” Machine Learning 45, pp. 5–32, 2001.
[13]	Zhi-Hua Zhou and Ji Feng, “Deep Forest: Towards An Alternative to Deep Neural Networks,” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3553–3559, 2017, doi: 10.24963/ijcai.2017/497.
[14]	MPU-9250, https://www.invensense.com/products/motion-tracking/9-axis/mpu-9250/
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[16]	TreeBagger, MathWorks, https://jp.mathworks.com/ help/stats/treebagger.html?lang=en.

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APA Style

Kazuki Zushida, Zhang Haohao, Hideaki Shimamura, Kazuhiro Motegi, Yoichi Shiraishi. (2021). Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower. International Journal of Mechanical Engineering and Applications, 9(1), 6-14. https://doi.org/10.11648/j.ijmea.20210901.12

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ACS Style

Kazuki Zushida; Zhang Haohao; Hideaki Shimamura; Kazuhiro Motegi; Yoichi Shiraishi. Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower. Int. J. Mech. Eng. Appl. 2021, 9(1), 6-14. doi: 10.11648/j.ijmea.20210901.12

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AMA Style

Kazuki Zushida, Zhang Haohao, Hideaki Shimamura, Kazuhiro Motegi, Yoichi Shiraishi. Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower. Int J Mech Eng Appl. 2021;9(1):6-14. doi: 10.11648/j.ijmea.20210901.12

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@article{10.11648/j.ijmea.20210901.12,
  author = {Kazuki Zushida and Zhang Haohao and Hideaki Shimamura and Kazuhiro Motegi and Yoichi Shiraishi},
  title = {Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {9},
  number = {1},
  pages = {6-14},
  doi = {10.11648/j.ijmea.20210901.12},
  url = {https://doi.org/10.11648/j.ijmea.20210901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20210901.12},
  abstract = {This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.},
 year = {2021}
}

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TY  - JOUR
T1  - Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower
AU  - Kazuki Zushida
AU  - Zhang Haohao
AU  - Hideaki Shimamura
AU  - Kazuhiro Motegi
AU  - Yoichi Shiraishi
Y1  - 2021/02/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmea.20210901.12
DO  - 10.11648/j.ijmea.20210901.12
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 6
EP  - 14
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20210901.12
AB  - This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.
VL  - 9
IS  - 1
ER  -

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Author Information

Kazuki Zushida

Department of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, Ohta, Gunma, Japan
Zhang Haohao

Department of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, Ohta, Gunma, Japan
Hideaki Shimamura

Life Creation Center, Honda R&D Co., ltd., Asaka, Saitama, Japan
Kazuhiro Motegi

Department of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, Ohta, Gunma, Japan
Yoichi Shiraishi

Department of Mechanical Science and Technology, Graduate School of Science and Technology, Gunma University, Ohta, Gunma, Japan

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APA Style

Kazuki Zushida, Zhang Haohao, Hideaki Shimamura, Kazuhiro Motegi, Yoichi Shiraishi. (2021). Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower. International Journal of Mechanical Engineering and Applications, 9(1), 6-14. https://doi.org/10.11648/j.ijmea.20210901.12

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ACS Style

Kazuki Zushida; Zhang Haohao; Hideaki Shimamura; Kazuhiro Motegi; Yoichi Shiraishi. Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower. Int. J. Mech. Eng. Appl. 2021, 9(1), 6-14. doi: 10.11648/j.ijmea.20210901.12

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AMA Style

Kazuki Zushida, Zhang Haohao, Hideaki Shimamura, Kazuhiro Motegi, Yoichi Shiraishi. Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower. Int J Mech Eng Appl. 2021;9(1):6-14. doi: 10.11648/j.ijmea.20210901.12

Copy | Download

@article{10.11648/j.ijmea.20210901.12,
  author = {Kazuki Zushida and Zhang Haohao and Hideaki Shimamura and Kazuhiro Motegi and Yoichi Shiraishi},
  title = {Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {9},
  number = {1},
  pages = {6-14},
  doi = {10.11648/j.ijmea.20210901.12},
  url = {https://doi.org/10.11648/j.ijmea.20210901.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20210901.12},
  abstract = {This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.},
 year = {2021}
}

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TY  - JOUR
T1  - Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower
AU  - Kazuki Zushida
AU  - Zhang Haohao
AU  - Hideaki Shimamura
AU  - Kazuhiro Motegi
AU  - Yoichi Shiraishi
Y1  - 2021/02/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmea.20210901.12
DO  - 10.11648/j.ijmea.20210901.12
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 6
EP  - 14
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20210901.12
AB  - This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.
VL  - 9
IS  - 1
ER  -

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