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



In this manuscript, we propose an on-line trajectory-tracking 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, an 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.


Mobile robot navigation RFID systems Optimal control Trajectory tracking Robot stabilization Nonholonomic systems 

Frequently Used Symbols


Feedback control gain at time t


Hamiltonian’s gradient with respect to K


Number of RFID tags in the environment


Costate variable (Lagrange multiplier)

q(t), qd(t)

Robot’s actual and desired pose at time t

\(\mathbf {q}_t^j\in \mathbb{R} ^3\)

jth tag position in 3D space


Initial and final time instants

I ≡ [t0, tf]

Time interval


Trace of matrix [⋅]


Robot’s control input vector at time t


Robot’s actuator noise at time t


Measurement noise vector at time t

(⋅)o, (⋅)ε, (⋅)ad

Optimal, perturb, admissible value of (⋅)

Lebesgue measurable function space


Gateaux (directional) derivative of J in direction ν


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Machine Intelligence, Robotics, and Mechatronics (MIRaM) Lab, School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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