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Naoki Morisawa, Masaya Imanishi, Masaki Yanagishita, Teiichirou Chiba, Hiroshi Yamamoto, Takeshi Hashimoto, and Daisuke Endo
In these days, construction sites are facing workforce shortages caused by declining labor and lack of young workers. Therefore, “Improve Productivity” is very important. Automation and autonomy of construction machinery is one of the solutions. As above, ICT hydraulic excavators and bulldozers are widely used in many sites, but the scope of the application is limited. In particularly, automated digging in hydraulic excavators still has many difficulties in actual use. The root cause is needed to the complicated operation during digging affected by the soil conditions. Since the soil conditions change anytime, if the operation is not adjusted to match to them, the efficiency will be decreased significantly. In other words, to achieve automated digging, the machine must recognize variation of soil conditions first. While digging, the operator has been sensing the machine's response affected by the soil conditions to consider it and adjusting machine operation simultaneously. In other words, human senses were used to recognized soil conditions to perform efficient operations. In that case, we thought that if we could replace human senses with internal sensors of the machine, the machine could recognize the soil conditions by itself. In this study, based on this hypothesis, we conducted measuring sensor data during digging in various soil conditions (e.g. hardness, materials…). The test results indicated that the soil hardness measured by simplified N-value meter, has a high correlation with the work efficiency and bucket tooth speed calculated from internal sensor data. This implicated that the sensor data from the excavator can be used to be determine soil conditions. In addition, there is a possibility that machine could recognize soil conditions by itself.
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