Development of a multisensor-based residual power prediction system for mobile robots

K. Su, J. Tzou, C. Liu
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引用次数: 8

Abstract

Based on the measurements of multiple sensors, this paper presents a residual power detection system for a mobile robot. We use four current sensors to measure the current variety of the mobile robot, and use multilevel multisensor fusion method to detect and diagnosis current sensor and voltage signals status. Moreover, a two level method is used to isolate faulty measured value such that more exact current status to be obtained. In this method, a redundant management method and a statistical prediction method are used in levels one and two, respectively. We use the same method to measure voltage of power system for mobile robots, and isolate faulty measured values. We design the power detection and isolation module using HOLTEK microchip according to the redundant management method. This module can transmit measured value and decision output to main controller (IPC) using series interface (RS232). However, it is possible that this method is faulty. In this case, the IPC can decide an exact power measured value according the statistical signal prediction method. Then we can predict the residual power of mobile robots using polynomial regression algorithm. Finally, we implement the proposed method on the experiment scenario of can set a power threshold value to calculate the critical time for the mobile robot. Meanwhile, experimental results are given to show the feasibility of the proposed method.
基于多传感器的移动机器人剩余功率预测系统的研制
提出了一种基于多传感器测量的移动机器人剩余功率检测系统。采用4个电流传感器测量移动机器人的电流变化,并采用多级多传感器融合方法检测和诊断传感器的电流和电压信号状态。此外,采用两电平方法隔离故障测量值,从而获得更精确的电流状态。在该方法中,一级采用冗余管理方法,二级采用统计预测方法。我们用同样的方法对移动机器人的电力系统电压进行了测量,并隔离了错误的测量值。根据冗余管理方法,采用HOLTEK微芯片设计电源检测与隔离模块。该模块通过串行接口(RS232)将测量值和判决输出传输到主控制器(IPC)。但是,这种方法有可能是错误的。在这种情况下,IPC可以根据统计信号预测方法确定准确的功率测量值。然后利用多项式回归算法对移动机器人的剩余功率进行预测。最后,我们在可以设置功率阈值的实验场景下实现了所提出的方法来计算移动机器人的临界时间。实验结果表明了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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