Estimation of Lawn Grass Lengths based on Random Forest Algorithm for Robotic Lawn Mower

Zushida, Kazuki; Zhang, Haohao; Shimamura, Hideaki; Motegi, Kazuhiro; Shiraishi, Yasuyuki

doi:10.23919/sice48898.2020.9240338

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Table 4 summarizes AI-equipped DTs and categorizes them based on learning algorithms used and subfields, such as control (detailed in Section 3.2), planning (detailed in Section 3.3), and HRI/HRC (detailed in Section 3.4). ANN Enabling industrial robots to bypass obstacles [272] LSTM Visual question answering for HMC system [273] Supervised and Unsupervised Learning FFT-PCA-SVM HRI welding and welder behavior analysis (identifying the professional level) [274] Reinforcement Learning DDPG COVID-19, improve efficiency in assembling medical equipment [275] Predictive Maintenance Supervised Learning DNN System health monitoring [276] Maximizing the overall plant availability of modern manufacturing systems [277] Workspace Modeling Supervised Learning Monte Carlo method Simulating the workspace of the mechanisms [278] Others Supervised Learning RF Estimation of lawn grass lengths for robotic lawn mower [279] PCA: Principal Component Analysis; GD: Gradient Descent; QP: Quadratic Programming; MACG: MultiAgent Computational Guidance; FFT: Fast Fourier Transform; UAV: Unmanned Aerial Vehicle.…”

Section: Overviewmentioning

confidence: 99%

“…Similarly, in [277], Aivaliotis et al integrated degradation curves in the predictive maintenance of industrial robots. Some other applications, such as using the Monte Carlo learning method in calculating the workspace of a serial robot manipulator [278] and random forest based estimation of lawn grass lengths for robotic lawn mower [279], can be found in Table 4.…”

Section: Robot Maintenance and Other Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

167

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Overviewmentioning

confidence: 99%

Section: Robot Maintenance and Other Applicationsmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

167

View full text Add to dashboard Cite

show abstract

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

Kojima

Shimamura

Motegi

et al. 2020

2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

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Estimation of Lawn Grass Lengths based on Random Forest Algorithm for Robotic Lawn Mower

Cited by 4 publications

References 5 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

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

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

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