Mapping and localization with RFID technology

Hähnel, Dirk; Burgard, Wolfram; Fox, Dieter; Fishkin, Kenneth P.; Philipose, Matthai

doi:10.1109/robot.2004.1307283

Cited by 565 publications

(347 citation statements)

References 6 publications

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“…Since an RFID system can recognize objects at high-speed and send data within various distances, RFID technology has been gaining popularity within the robot community [5]. Hahnel et al studied to improve robot localization with a pair of RFID antennas [6]. They presented a probabilistic measurement model for RFID readers that allow them to accurately localize the RFID tags in the environment.…”

Section: Related Studymentioning

confidence: 99%

A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication

Miah

Gueaieb

2011

Autonomous and Intelligent Systems

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Abstract. Most common techniques pertaining to the problem of mobile robot navigation are based on the use of multiple sensory data fusion and of excessive number of reference Radio Frequency (RF) stations. However, spatial layout, high navigation accuracy, and cost problems limit the applicability of those techniques in many modern robotic applications. The current manuscript outlines a novel computationally inexpensive indoor mobile robot navigation system with an intelligent processing of received signal strength (RSS) measurements of a customized Radio Frequency IDentification (RFID) system. The high navigation accuracy is achieved by incorporating a conventional stochastic Uncented Kalman Filter (UKF), which integrates RSS measurements from a customized RFID system and sensory data from the robot's wheel encoder. The workspace is defined by placing a number of RFID tags at 3-D locations. The orthogonal 2-D projection points of those tags on the ground define the target points where the robot is supposed to reach. The customized RFID system is simulated using a comprehensive electromagnetic commercial software, FEKO. The validity and suitability of the proposed navigation system are demonstrated by conducting extensive computer simulations in order to provide high-performance level in terms of accuracy and scalability.

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Section: Related Studymentioning

confidence: 99%

A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication

Miah

Gueaieb

2011

Autonomous and Intelligent Systems

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“…RFIDs have already been successfully utilized for localizing mobile robots ͑Hähnel, Burgard, Fox, Fishkin & Philipose, 2004;Bohn &Mattern, 2004͒ andemergency responders ͑Kantor et al, 2003;Miller et al, 2006͒. Hähnel andcolleagues ͑Hähnel et al, 2004͒ successfully utilized Markov localization for localizing a mobile robot in an office environment.…”

Section: Related Workmentioning

confidence: 99%

Real‐time localization and elevation mapping within urban search and rescue scenarios

Kleiner

Dornhege

2007

Journal of Field Robotics

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Urban search and rescue ͑USAR͒ is a time critical task. One goal in rescue robotics is to have a team of heterogeneous robots that explore autonomously the disaster area, while jointly creating a map of the terrain and registering victim locations, which can further be utilized by human task forces for rescue. Basically, the robots have to solve autonomously in real-time the problem of simultaneous localization and mapping ͑SLAM͒, consisting of a continuous state estimation problem and a discrete data association problem. In this paper we contribute a novel method for efficient loop closure in harsh large-scale environments that utilizes RFID technology for data association and slippage-sensitive odometry for 2D pose tracking. Furthermore, we introduce a computational efficient method for building elevation maps by utilizing an extended Kalman filter for 3D pose tracking, which can be applied in real-time while navigating on rough terrain. The proposed methods have been extensively evaluated within outdoor environments, as well as within USAR test arenas designed by the National Institute of Standards and Technology ͑NIST͒. Our results show that the proposed methods perform robustly and efficiently within the utilized benchmark scenarios.

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“…RFID technology drew the attention of a large body of research on mobile robot localization owing to its wide availability, contactless recognition ability, and affordability [50,36,20,10]. In most cases, RFID systems are deployed for solving localization problem (not stabilization or tracking problems) of mobile robots in a particular environment [21,44,31,35].…”

Section: Introductionmentioning

confidence: 99%

RFID-Based Mobile Robot Trajectory Tracking and Point Stabilization Through On-line Neighboring Optimal Control

Miah

Gueaieb

2014

J Intell Robot Syst

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In this manuscript, we propose an on-line trajectorytracking algorithm for nonholonomic Differential-Drive Mobile Robots (DDMRs) in the presence of possibly large parametric and measurement uncertainties. Most mobile robot tracking techniques that depend on reference RF beacons rely on approximating line-of-sight (LOS) distances between these beacons and the robot. The approximation of LOS is mostly performed using Received Signal Strength (RSS) measurements of signals propagating between the robot and RF beacons. However, accurate mapping between RSS measurements and LOS distance remains a significant challenge and is almost impossible to achieve in an indoor reverberant environment. This paper contributes to the development of a neighboring optimal control strategy where the two major control tasks, trajectory tracking and point stabilization, are solved and treated as a unified manner using RSS measurements emitted from Radio Frequency IDentification (RFID) tags. The proposed control scheme is divided into two cascaded phases. The first phase provides the robot's nominal control inputs (speeds) and its trajectory using full-state feedback. In the second phase, we design the neighboring optimal controller, where RSS measurements are used to better estimate the robot's pose by employing an optimal filter. Simulation and experimental results are presented to demonstrate the performance of the proposed optimal feedback controller for solving the stabilization and trajectory tracking problems using a DDMR. Keywords Mobile robot navigation · RFID systems · optimal control · trajectory tracking · robot stabilization · nonholonomic systems. Frequently Used Symbols K(t)Feedback control gain at time t H K Hamiltonian's gradient with respect to K s Number of RFID tags in the environment ψ Costate variable (Lagrange multiplier)Robot's actual and desired pose at time t q j t ∈ R 3 jth tag position in 3D space t 0 ,t f Initial and final time instantsRobot's control input vector at time t ξ (t) Robot's actuator noise at time t ζ (t)Measurement noise vector at time t (·) o , (·) ε , (·) ad Optimal, perturb, admissible value of (·) L Lebesgue measurable function space ν T dJ(·) Gateaux (directional) derivative of J in direction ν 1 Introduction

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Mapping and localization with RFID technology

Cited by 565 publications

References 6 publications

A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication

A Fuzzy Logic Approach for Indoor Mobile Robot Navigation Using UKF and Customized RFID Communication

Real‐time localization and elevation mapping within urban search and rescue scenarios

RFID-Based Mobile Robot Trajectory Tracking and Point Stabilization Through On-line Neighboring Optimal Control

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