Reference Tracking of a Nonholonomic Mobile Robot using Sensor Fusion Techniques and Linear Control

Forte, Marcus Davi do Nascimento; Correia, Wilkley Bezerra; Nogueira, Fabrício G.; Torrico, Bismark C.

doi:10.1016/j.ifacol.2018.06.092

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…It can be readily seen from (10) that different feedforward velocities lead to different models. Linear controller from (Forte et al, 2018) is suitable for a constant set of velocities but may exhibit lack of robustness if variations in feedforward inputs are needed. So that, natural choices include either non-linear or LPV controllers which are capable of keeping a desired performance over a set of operating conditions.…”

Section: Mobile Robot Kinematic Modelmentioning

confidence: 99%

“…This motivates the use of a sensor fusion model that would combine the advantages of both accelerometer and gyroscope. The fusion model is similar to the yaw estimation presented in (Forte et al, 2018), but with the measurement angle coming from the accelerometer. The model consists in estimating the time-varying bias from the gyroscope and removing it from the integration based on the accelerometer measurement.…”

Section: Fusion Model For Pitch Estimationmentioning

confidence: 99%

“…Trajectory tracking of a NWMR is a nonlinear problem that may be tackled using either non-linear (Klancar et al, 2005) or linear (Forte et al, 2018) controllers over a linearized operating point. A Lyapunov-based stability study is performed in (Tzafestas, 2014), where it is proved that a simple set of positive error state feedback gains may guarantee system stability.…”

Section: Introductionmentioning

confidence: 99%

“…Most works use the effective sensor fusion techniques and Inertial Measuring Units (IMUs) to combine sensory data from different sensors so that the limitation of individual sensors are compensated. The work in (Forte et al, 2018), for example, proposes a sensor fusion technique to estimate the heading of a NWMR using a magnetometer and a gyroscope which copes with the heavily noisy magnetic measurements and biased gyroscopic measurements.…”

Section: Introductionmentioning

confidence: 99%

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LPV Controller Design for Terrestrial Mobile Robot With Tilt Compensation

Filho

Forte

Torrico

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This work presents an LPV (Linear Parameter Varying) controller design for the trajectory tracking problem of nonholonomic wheeled mobile robots (NWMR). The scheduling varying parameters are defined as the feedforward velocities which the robot has to track according to a desired trajectory. The proposed model also includes the robot's motion over a tilted environment. A tilt-compensation scheme is used to maintain the ground speed of the robot if the robot is subjected to an inclination. The compensation is computed on the feedforward velocities. For practical purposes, the inclination angle is measured by an Inertial Measuring Unit. Thus, a sensor fusion model is proposed to fuse data from an accelerometer and a gyroscope. Simulation results are presented in order to evaluate the performance of the controller, sensor fusion and tilt-compensation models.

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Section: Mobile Robot Kinematic Modelmentioning

confidence: 99%

Section: Fusion Model For Pitch Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LPV Controller Design for Terrestrial Mobile Robot With Tilt Compensation

Filho

Forte

Torrico

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

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“…It is applied in various settings such as navigation/ localization/positioning for both indoor [7,8] or outdoor [9][10][11] as well as in pedestrian dead-reckoning [12,13], mobile robots [14,15], and even for research works related to extraterrestrial navigational purposes [16,17]. Sensor fusion was used in the design of an assistive instrument for paraglider and hang-glider pilots [18].…”

Section: Introductionmentioning

confidence: 99%

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

Vista

Chong

2019

Journal of Sensors

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This paper describes the design and real-time implementation of a proposed algorithm for deriving an accurate heading system by fusing data from various inexpensive sensor devices that is comparable to more expensive maritime navigation systems. The proposed algorithm is a 3-Stage Classification N’ Weighing (CnW) Heading System with forward azimuth (FAz) and extended Kalman filter (EKF). Data from three Global Positioning System devices, an inertial measurement unit, and an electronic compass were fed into the algorithm that can be generally described as Classification N’ Weighing-Stage 1 → forward azimuth → Classification N’ Weighing-Stage 2 → extended Kalman filter → Classification N’ Weighing-Stage 3. The proposed algorithm is shown to be comparably accurate as an expensive marine navigation system, and it has less processing time compared to our previous work. The Qt-anywhere-based system developed on a Linux desktop was successfully downloaded onto an Ubuntu Linux-embedded board for real-time implementation. Important notes related to device naming problems when deploying the system on a Linux-embedded board are also given as reference for those interested to address it.

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