NDLI: Development an intelligent power detection system for mobile robots

Content Provider	IEEE Xplore Digital Library
Author	Kuo, L.S. Lin Yungchin Sheng, V.S. Jheng, S.J.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Nat. Yunlin Univ. of Sci. & Technol., Douliou (Kuo, L.S.; Lin Yungchin; Sheng, V.S.; Jheng, S.J.)
Abstract	The paper presents an intelligent 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 current sensor and voltage signals status. Moreover, a two level method is used to isolate faulty measured value such that more exact current and voltage 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 design the power detection and isolation module using HOLTEK microchip according to the redundant management method. This module can transmit measured value and estimated statue 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 and faulty status 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.
Starting Page	637
Ending Page	641
File Size	336585
Page Count	5
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605684
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Voltage measurement Power measurement Polynomial regression algorithm Intelligent power detection system Statistical signal prediction method Current measurement Sensors Mobile robots Mobile robot Artificial intelligence Robots Redundant management method
Content Type	Text
Resource Type	Article

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