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A simulation study of sensor data fusion using UKF for bucket wheel reclaimer localization

机译:UKF用于斗轮取料机定位的传感器数据融合的仿真研究

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Bucket Wheel Reclaimers (BWRs) normally travel on a rail among stockpiles to perform stacking and reclaiming operations. Currently, the position accuracy of the bucket wheel at the end of boom measured by the onboard encoder system is limited to 30cm. To maintain such accuracy, calibrated points have to be placed along the rail, which is inefficient and costly. This paper proposes a simulation study using Unscented Kalman Filter (UKF) algorithm to fuse DGPS and encoder data for BWR localization. The results obtained indicate that the errors in positional accuracy are better than 15cm and UKF is an objective technology that can be applied to localize such large scaled machine.
机译:斗轮取料机(BWR)通常在库存中的轨道上行驶以执行堆放和取料操作。当前,通过车载编码器系统测量的动臂末端铲斗轮的位置精度限制为30cm。为了保持这样的精度,必须沿着轨道放置校准点,这是低效且昂贵的。本文提出了使用无味卡尔曼滤波器(UKF)算法融合DGPS和编码器数据以进行BWR定位的仿真研究。获得的结果表明,位置精度的误差优于15cm,UKF是可用于对这种大型机器进行定位的客观技术。

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