Comparison of Shallow Neural Network with Random Forest Algorithm in Estimating Lawn Grass Lengths for Robotic Lawn Mowers

Kojima, Masaki; Shimamura, Hideaki; Motegi, Kazuhiro; Shiraishi, Yasuyuki

doi:10.23919/sice48898.2020.9240471

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…In order to precisely control the rotation speed of motor, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The authors are now promoting the research and development on autonomous driving and group control for work vehicles [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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

Zushida¹,

Zhang²,

Shimamura³

et al. 2021

IJMEA

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zushida¹,

Zhang²,

Shimamura³

et al. 2021

IJMEA

Self Cite

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Estimating Ground Conditions for Efficient Operation of Robotic Lawn Mowers

Zushida

Zhang

Kojima³

et al. 2022

Journal of Japan Institute of Electronics Packaging

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AI-Based Approach for Lawn Length Estimation in Robotic Lawn Mowers

Shiraishi¹,

Zhang²,

Motegi³

2021

Robotics Software Design and Engineering

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This chapter describes a part of autonomous driving of work vehicles. This type of autonomous driving consists of work sensing and mobility control. Particularly, this chapter focuses on autonomous work sensing and mobility control of a commercial electric robotic lawn mower, and proposes an AI-based approach for work vehicles such as a robotic lawn mower. These two functions, work sensing and mobililty control, have a close correlation. In terms of efficiency, the traveling speed of a lawn mower, for example, should be reduced when the workload is high, and vice versa. At the same time, it is important to conserve the battery that is used for both work execution and mobility. Based on these requirements, this chapter is focused on developing an estimation system for estimating lawn grass lengths or ground conditions in a robotic lawn mower. To this end, two AI algorithms, namely, random forest (RF) and shallow neural network (SNN), are developed and evaluated on observation data obtained by a fusion of ten types of sensor data. The RF algorithm evaluated on data from the fusion of sensors achieved 92.3% correct estimation ratio in several experiments on real-world lawn grass areas, while the SNN achieved 95.0%. Furthermore, the accuracy of the SNN is 94.0% in experiments where sensor data are continuously obtained while the robotic lawn mower is operating. Presently, the proposed estimation system is being developed by integrating two motor control systems into a robotic lawn mower, one for lawn grass cutting and the other for the robot’s mobility.

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Comparison of Shallow Neural Network with Random Forest Algorithm in Estimating Lawn Grass Lengths for Robotic Lawn Mowers

Cited by 3 publications

References 3 publications

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

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

Estimating Ground Conditions for Efficient Operation of Robotic Lawn Mowers

AI-Based Approach for Lawn Length Estimation in Robotic Lawn Mowers

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