SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter

Tang, Ming; Chen, Zhe; Yin, Fuliang

doi:10.1007/s12555-020-0896-5

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…In contrast to the four-wheeled robots with complex structures and high steering control requirements, we propose a low-cost, efficient two-wheeled robot design with a caster wheel. For example, the works in [11] applied SLAM with the Improved Schmidt Orthogonal UKF algorithm on Cark Park dataset to validate the efficacy of their design. The authors in [36] employed trajectory tracking control for wheeled mobile robots with kinematic parameter uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…They estimated the position and motion of a robot moving with a constant velocity, acceleration, and turn rate, as well as its orientation. On similar lines of research, the authors in [11], implemented the path planning of aerial vehicles using the UKF [11]. Next, the team of researchers in [12] used the Visual SLAM algorithm that allows Universal Asynchronous Receiver Transmitter (UART) communication to obtain the real-time pose coordinates of a robot.…”

Section: Introductionmentioning

confidence: 99%

“…From the above discussion, it is apparent that the researchers extended the applications of EKF and UKF for optimizing the paths of robots on a linear as well as non-linear paths [6,8,10,11]. But, calculating the deviation of a robot from its actual path when it moves on a curvy path is underexplored.…”

Section: Introductionmentioning

confidence: 99%

“…The second step applies a measurement model to estimate the updated state of a robot. This results in a blending of forces and modifies the robot to establish quick control [6,10,11]. In addition to applying EKF and UKF, the authors modified the EKF by augmenting its results with values of environment coefficient obtained from various sensors.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

Mittal,

Rani,

Pathak

et al. 2023

Computers

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The automation industry faces the challenge of avoiding interference with obstacles, estimating the next move of a robot, and optimizing its path in various environments. Although researchers have predicted the next move of a robot in linear and non-linear environments, there is a lack of precise estimation of sectorial error probability while moving a robot on a curvy path. Additionally, existing approaches use visual sensors, incur high costs for robot design, and ineffective in achieving motion stability on various surfaces. To address these issues, the authors in this manuscript propose a low-cost and multisensory robot capable of moving on an optimized path in diverse environments with eight degrees of freedom. The authors use the extended Kalman filter and unscented Kalman filter for localization and position estimation of the robot. They also compare the sectorial path prediction error at different angles from 0° to 180° and demonstrate the mathematical modeling of various operations involved in navigating the robot. The minimum deviation of 1.125 cm between the actual and predicted path proves the effectiveness of the robot in a real-life environment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

Mittal,

Rani,

Pathak

et al. 2023

Computers

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“…Real-time performance is required in SOC estimation, so computational complexity can be reduced by using Schmidt orthogonal transformation sampling. 18 The aforementioned Kalman filter series procedures assume Gaussian white noise with a zero mean for both the observation noise in the observation equation and the process noise in the state equation. The noise covariance matrix is typically set to a constant in practical applications, which can lead to mistakes in estimating battery SOC.…”

mentioning

confidence: 99%

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn_0.6Fe_0.4PO₄/LiNi_0.5Co_0.2Mn_0.3O₂ Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Xiao,

Xiong,

Lei

et al. 2023

J. Electrochem. Soc.

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For the use of batteries to be secure and dependable, monitoring the state of charge (SOC) is essential. It is challenging to accurately calculate the SOC of LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 batteries because they have two flat voltage plateaus and their performance is significantly influenced by temperature. To improve SOC estimation accuracy, a battery-based SOC research approach based on dual Kalman filtering is put forth. The improved adaptive unscented Kalman filter (MIASOUKF) is utilized for SOC estimation, while the extended Kalman filter (EKF) is employed for online identification of battery model parameters based on the second-order RC equivalent circuit model. By combining adaptive unscented Kalman filtering (AUKF) and multi-innovation, the estimation accuracy of the algorithm is improved by repeating the use of historical information, and the sampling points are subjected to Schmidt orthogonal transformation during the sampling point selection in AUKF to decrease the computational complexity of the AUKF, making the algorithm easy to implement in real time. Under complex working conditions, the average error of the MIASOUKF-EKF joint estimation algorithm is less than 0.53%. Through the comparison of experiments and simulations, MIASOUKF-EKF algorithm can effectively improve the estimation accuracy of SOC.

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Auditory Feature Driven Model Predictive Control for Sound Source Approaching

Wang,

Zou,

Zhang

et al. 2024

Int. J. Control Autom. Syst.

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SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter

Cited by 8 publications

References 21 publications

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn_0.6Fe_0.4PO₄/LiNi_0.5Co_0.2Mn_0.3O₂ Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Auditory Feature Driven Model Predictive Control for Sound Source Approaching

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SLAM with Improved Schmidt Orthogonal Unscented Kalman Filter

Cited by 8 publications

References 21 publications

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

Low-Cost Multisensory Robot for Optimized Path Planning in Diverse Environments

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Auditory Feature Driven Model Predictive Control for Sound Source Approaching

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Product

Resources

About

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn_0.6Fe_0.4PO₄/LiNi_0.5Co_0.2Mn_0.3O₂ Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation