A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion
Published Wednesday, 5 October 2011 by gk inA Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion
Introduction of embedded:
Embedded system debugging involves more conceptual layers of a target system than debugging for time-sharing systems. Consider the case of debugging a C program within a time-sharing system. User-debugger interaction occurs almost entirely at a C language level of abstraction. Descent into assembly language and machine code representations of a target program is rare. Suspicions about a compiler bug may require inspection of generated assembly code.
About robotics
A robot is a mechanical intelligent agent which can perform tasks on its own, or with guidance. In practice a robot is usually an electro-mechanical machine which is guided by computer and electronic programming. Robots can be autonomous or semi-autonomous and come in those two basic types: those which are used for research into human-like systems, such as ASIMO and TOPIO, as well as those into more defined and specific roles, such as Nano robots and Swarm robots; and helper robots which are used to make or move things or perform menial or dangerous tasks, such as Industrial robots or mobile or servicing robots.
Abstract:
This paper addresses radio-frequency identification (RFID)-based mobile robot localization which adopts RFID tags distributed in a space. Existing stand-alone RFID systems for mobile robot localization are hampered by many uncertainties. Therefore, we propose a novel algorithm that improves the localization by fusing an RFID system with an ultrasonic sensor system. The proposed system partially removes the uncertainties of RFID systems by using distance data obtained from ultrasonic sensors. We define a global position estimation (GPE) process using an RFID system and a local environment cognition (LEC) process using ultrasonic sensors. Then, a hierarchical localization algorithm is proposed to estimate the position of the mobile robot using both GPE and LEC. Finally, the utility of the proposed algorithm is demonstrated through experiments.
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