State estimation based on Kalman filtering techniques in navigation

Tamás, Levente; Lazea, Gheorghe; Robotin, R.; Marcu, C.; Herle, S.; Szekely, Zoltan

doi:10.1109/aqtr.2008.4588811

Cited by 14 publications

(3 citation statements)

References 14 publications

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“…In this section, we simulate and validate the performance of the Unscented Kalman Filter (UKF) [15,31,32,33,34] and Particle Filter (PF) [19,20,21,22,23] used to state estimate Lithium-ion Battery in [4] in the presence of noise in both the process (state) and the sensor (input) variables. The injected noise is listed in table 1.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Comparison of State-Estimation Algorithms for a Noise-Injected Lithium-ion Battery System

Bjaili¹,

Rushdi²,

Moinuddin³

2017

BJMCS

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This paper deals with one of the most prominent problems in industrial prognostics, namely the estimation of the Remaining Useful Life (RUL) of the most popular industrial battery, viz., the lithium-ion battery. The paper presents a state-space model of the battery, and then estimates the dynamic behavior of seven of its process variables and two of its sensor variables. The estimation is achieved via two well known estimators, the Unscented Kalman Filter (UKF) and the Particle Filter (PF) when noise of various levels and types is injected. Numerical and chart comparisons of these two computing estimators are reported and discussed.

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Section: Simulation and Resultsmentioning

confidence: 99%

Comparison of State-Estimation Algorithms for a Noise-Injected Lithium-ion Battery System

Bjaili¹,

Rushdi²,

Moinuddin³

2017

BJMCS

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“…According to Kalman's formulation [14], the Q and R noise covariance matrices parameters are adjusted empirically [20]. It must be noted that the measurements from the accelerometer are slightly smoothed by an integrated hardware filter.…”

Section: Robot Control Systemmentioning

confidence: 99%

Complete Low-Cost Implementation of a Teleoperated Control System for a Humanoid Robot

Cela

Yebes

Arroyo

et al. 2013

Sensors

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Humanoid robotics is a field of a great research interest nowadays. This work implements a low-cost teleoperated system to control a humanoid robot, as a first step for further development and study of human motion and walking. A human suit is built, consisting of 8 sensors, 6 resistive linear potentiometers on the lower extremities and 2 digital accelerometers for the arms. The goal is to replicate the suit movements in a small humanoid robot. The data from the sensors is wirelessly transmitted via two ZigBee RF configurable modules installed on each device: the robot and the suit. Replicating the suit movements requires a robot stability control module to prevent falling down while executing different actions involving knees flexion. This is carried out via a feedback control system with an accelerometer placed on the robot's back. The measurement from this sensor is filtered using Kalman. In addition, a two input fuzzy algorithm controlling five servo motors regulates the robot balance. The humanoid robot is controlled by a medium capacity processor and a low computational cost is achieved for executing the different algorithms. Both hardware and software of the system are based on open platforms. The successful experiments carried out validate the implementation of the proposed teleoperated system.

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“…In another study, a relative localization method was used to determine the navigational route of a convoy of robotic units in an indoor environment using an EKF based on a lowcost laser range system and built-in odometric sensors [2]. In skid-steered vehicles, discrepancies remain between position measurements and the navigational estimations obtained from odometry, infrared, and ultrasonic measurements and Kalman filtering techniques [3]. However, this study only mentioned the advantages of the different extensions of the KF for nonlinear estimations.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control of a Mobile Robot by Using Dual Estimation Algorithm

Choi

2013

Advances in Mechanical Engineering

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Skid-steered mobile robots have been developed to explore unknown environments, especially in rough terrain situations, where the wheel slips always occur and vary when the robot is traveling with different trajectory shapes. The wheel slippage limits the traction and braking abilities of the robot. In this paper, we propose a new dual estimation algorithm to overcome the previous limitations. The estimated values such as the robot's positions and wheel slips are obtained based on the Kalman filtering technique. As demonstrated by our experimental results, the advantages of the new method are effective to overcome the slip limitations in the path tracking control problem of a four-track wheel skid-steered mobile robot (4-TW SSMR).

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State estimation based on Kalman filtering techniques in navigation

Cited by 14 publications

References 14 publications

Comparison of State-Estimation Algorithms for a Noise-Injected Lithium-ion Battery System

Comparison of State-Estimation Algorithms for a Noise-Injected Lithium-ion Battery System

Complete Low-Cost Implementation of a Teleoperated Control System for a Humanoid Robot

Path Tracking Control of a Mobile Robot by Using Dual Estimation Algorithm

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