An Intelligent Mobile Robot Navigation Technique Using RFID Technology

Gueaieb, Wail; Miah, Suruz

doi:10.1109/tim.2008.919902

Cited by 140 publications

(66 citation statements)

References 15 publications

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“…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).…”

Section: Theorem 1 (Necessary Conditions Of Optimality)mentioning

confidence: 99%

“…In the following, we numerically solve for the gain K such that (19) is satisfied, aggregating the components described earlier.…”

Section: Theorem 1 (Necessary Conditions Of Optimality)mentioning

confidence: 99%

“…Yet, they are based on known noise statistics and require complex image processing techniques. In 2008, Gueaieb and Miah pioneered a navigation algorithm, where the phase difference of RFID signals is exploited to navigate a mobile robot in an indoor environment [19]. The navigation system is, however, based on a customized RFID reader (not RFID tag) architecture and the navigation performance is evaluated using computer simulations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Theorem 1 (Necessary Conditions Of Optimality)mentioning

confidence: 99%

“…In the following, we numerically solve for the gain K such that (19) is satisfied, aggregating the components described earlier.…”

Section: Theorem 1 (Necessary Conditions Of Optimality)mentioning

confidence: 99%

Section: Introductionmentioning

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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“…Thus, the necessary conditions of optimality are given by the integral inequality (21), the costate dynamics (24), and the state equation (8). In other words, the choice of K ∈ K ad determines the optimality conditions (21), (24), and (8).…”

Section: Optimal Feedback Control Lawmentioning

confidence: 99%

“…Hahnel et al tried to improve the localization accuracy with a pair of RFID antennas [18]. The phase-difference approach of navigation mobile robots was first introduced by Gueaieb and Miah in [24], where the RFID reader mounted on the robot was customized. The fuzzy logic controller was fed by the phase-difference received at the reader.…”

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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A Rule-Based Expert System to Decide on Direction and Speed of a Powered Wheelchair

Sanders

Gegov

Haddad

et al. 2018

Advances in Intelligent Systems and Computing

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Some rule based techniques are presented that can assist powered wheelchair drivers. The expert system decides on the direction and speed of their wheelchair. The system tends to avoid obstacles while having a tendency to turn and head in towards a desired destination. This is achieved by producing a new target angle as an extra input. Other inputs are from sensors and a joystick. Directions are recommended and mixed with user inputs from the joystick representing desired direction and desired speed. The rule-based system decides on an angle to turn the powered wheelchair and suggest it. Inputs from the joystick and sensors are mixed with the suggested angle from the Rule Based Expert System. A modified direction for the wheelchair is produced. The whole system helps disabled wheelchair users to drive their powered wheelchairs.

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An Intelligent Mobile Robot Navigation Technique Using RFID Technology

Cited by 140 publications

References 15 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

A Rule-Based Expert System to Decide on Direction and Speed of a Powered Wheelchair

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