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首页> 外文会议>Annual Conference of the Society of Instrument and Control Engineers of Japan >Estimation of Lawn Grass Lengths based on Random Forest Algorithm for Robotic Lawn Mower
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Estimation of Lawn Grass Lengths based on Random Forest Algorithm for Robotic Lawn Mower

机译：基于随机森林算法的机器人割草机草坪草长估计

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This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm. This relates to Digital Twin and Virtual Twin of Hybrid Twin approach. Recently, the robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems. However, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized. 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. 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. By applying the random forest algorithm, the combination of sensing parameters attained more than 90% correct estimation ratio is shown through some experiments. Now, the suggested algorithm and the sensor fusion are evaluated against wide range of lawn and grounds.
机译：提出了一种基于随机森林算法的草坪草长或地面状况的估算方法。这涉及混合双胞胎方法的数字双胞胎和虚拟双胞胎。最近，随着高效传感器和嵌入式系统的出现，机器人割草机变得越来越流行。但是，无法识别草坪草的长度或诸如泥土，砾石或混凝土等地面条件。结果，从自动割草机的操作开始到结束，用于割草的电动机以恒定的转速运行。为了精确地控制电动机的转速，通过使用有效的传感器数据来估算草坪草的长度和地面条件。通过随机森林算法，通过一些实验证明了达到90％以上正确估计率的传感参数组合。现在，针对各种草坪和地面对所建议的算法和传感器融合进行了评估。

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