A Passive UHF-RFID System for the Localization of an Indoor Autonomous Vehicle

Giampaolo, E. Di; Martinelli, Francesco

doi:10.1109/tie.2011.2173091

Cited by 123 publications

(50 citation statements)

References 21 publications

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“…This is in contrast to many recent RFID-based techniques which usually tackle the localization problem only [16,6,50]. The localization accuracy reported there in is in the range of 0.1-0.5 m, despite neglecting the effect of reverberations and low signal-to-noise ratios.…”

Section: Resultsmentioning

confidence: 85%

“…The localization accuracy reported there in is in the range of 0.1-0.5 m, despite neglecting the effect of reverberations and low signal-to-noise ratios. Moreover, the RFID-based robot navigation techniques presented in those papers are mostly based on simulations, see [16], for example, and some references therein.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the problem boils down to finding K(t) t ∈ I , such that the robot's actual trajectory (13) and the costate trajectory from (18) satisfy (19). The optimal feedback control gain K o (t) can be determined by satisfying the Hamiltonian inequality (16). In other words, K(t) is to be adaptively tuned to minimize the robot's tracking error.…”

Section: Theorem 1 (Necessary Conditions Of Optimality)mentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Theorem 1 (Necessary Conditions Of Optimality)mentioning

confidence: 99%

See 1 more Smart Citation

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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“…It is worth mentioning that this controller is also able to stabilize the robot at a fixed configuration by simply tuning the weight matrices (P(t f ) and Q(t)) of the cost function (11). This is in contrast to many recent RFID-based techniques which usually tackle the localization problem only [21,39,16]. The localization accuracy reported there in is in the range of 0.1-0.5 m, despite neglecting the effect of reverberations and low signal-to-noise ratios.…”

Section: Resultsmentioning

confidence: 99%

“…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 [16,17,18,19,20,21]. Authors in [22,23] investigated the application of an RFID system in mobile robot localization by computing the flight time of active beacons' RF signals.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

Miah

Gueaieb

2014

ISA Transactions

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Most mobile robot navigation 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, to date, relying on RSS measurements for approximating LOS distance remains a significant challenge. Accurate mapping between RSS measurements and LOS distance is almost impossible to achieve in an indoor reverberant environment. In this paper, we design a partially-observed feedback controller for a differential drive mobile robot (DDMR) where the feedback signal is in the form of noisy RSS measurements emitted from radio frequency identification (RFID) tags placed in the environment. The proposed controller does not require an accurate mapping between the LOS distance and the RSS measurements from RFID tags. In addition, it takes into account the robot's actuator (speed) constraints. Unlike many other previously devised solutions, the proposed control scheme does not require the linearization of the nonlinear DDMR model. The performance of this method is demonstrated through both numerical simulations and real-time experiments.

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Localization with RFID

Zheng

Kaiser

2016

Digital Signal Processing for RFID

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A Passive UHF-RFID System for the Localization of an Indoor Autonomous Vehicle

Cited by 123 publications

References 21 publications

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

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

Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach

Localization with RFID

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