A fuzzy adaptive extended Kalman filter exploiting the student's t distribution for mobile robot tracking

Lai, Xin; Guorui, Zhu; Chambers, Jonathon A.

doi:10.1088/1361-6501/ac0ca9

Cited by 13 publications

(11 citation statements)

References 44 publications

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“…Kalman Filter is the heart of the SLAM process. Kalman filtering is an optimal state estimation algorithm that can realize linear minimum variance estimation for linear systems [5,6]. It updates where the robot thinks it is based on the landmarks, the KF corrects the odometry pose estimate in real-time considering the landmark measurement uncertainty.…”

Section: Building Map Based On Slammentioning

confidence: 99%

Application of A Grasping Autonomous Transport Robotic Car with Machine Vision in the Express Industry in China

Xiong¹

2023

HSET

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In recent years, COVID-19 has badly affected people’s lives, and most industries have been hit hard, the express industry is a typical example, if the staff needs to be quarantined for epidemic control, the work efficiency of the express transfer center would reduce. In this project, to solve problems in the express industry, an autonomous transport car with a robotic arm was designed by using the technology of machine vision, path planning, SLAM, etc. The robot was simulated in the ROS platform m replaced by the turtlebot3 robot. As the result, the robot will go to the position set by Python code in the gazebo, when the robot identified the target, it will keep going straight forward to the target while keeping the target in the center of the camera. If the distance between the camera and the target is 0.15 meters, the car will stop and the robotic arm would grasp the target automatically. After the grasp, the robot will go to the destination and put the target in the basket. The result of the simulation shows that this robot can be applied to the express transfer center.

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Section: Building Map Based On Slammentioning

confidence: 99%

Application of A Grasping Autonomous Transport Robotic Car with Machine Vision in the Express Industry in China

Xiong¹

2023

HSET

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“…where   k k p z |x is the measurement likelihood PDF, and its function is formulated as follows, (12) where the joint PDF of the k…”

Section: The Gaussian Approximate Filter (Gaf)mentioning

confidence: 99%

“…Although EKF exists dependent on first order linearization of system models to propagate state mean and covariance, linear processes and system accuracy decrease thus resulting in performance degradation and failure of running results [10]. However, researchers have made a lot of efforts to expand the scope of application of this technology [8][9][10][11][12][13]. Especially adaptive algorithm combined with EKF has been used to improve the accuracy of the system model and solve divergence problems caused by a single use of EKF.…”

Section: Introductionmentioning

confidence: 99%

“…Nourmohammadi and Keighobadi [17] proposed the adaptive integration scheme is proposed, which is robust to the performance of the attitude-heading reference system (AHRS) and can be used for the AHRS and the SINS according to vehicle maneuvering conditions. In or-der to solve this shortcoming, a novel fuzzy adaptive extended Kalman filter exploiting the Student's t distribution (FASTEKF) for a robot path tracking is proposed [12]. In FASTEKF, the distributions of process and measurement noise are processed by Student's t distribution.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Fuzzy Neural Network Based On Progressive Gaussian Approximate Filter with Variable Step Size

Zhu

2022

ITC

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The nonlinear filtering problem is a hot spot in robot navigation research. As an advanced method that can effectively improve the robustness and accuracy of the system, the progressive Gaussian approximate filter with variable step size (PGAFVS) still has some shortcomings, how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy, which becomes the purpose of this paper. This paper formulates the processing of trajectory tracking measurement noise problem as a Kalman filtering procedure and the measurement noise covariance matrix in controller, is jointly estimated based on the progressive Gaussian approximate filter (PGAF), after that, PGAFVS can be deduced. Then we proposed an adaptive fuzzy and backpropagation neural network controller based on PGAFVS (AFNPGA-VS) that can improve the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. The simulation results show that the proposed algorithm outperforms the state-of-the-art methods.

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“…Compared with the Gaussian distribution model, the Student's t-distribution is more tolerant of outliers and can better handle heavy-tailed noise [28]. Several tracking algorithms based on the Student's t-distribution model have been proposed [29,30]. Compared with neural network models, regression analysis models have faster data processing speed, require fewer hyperparameters, and are better adapted to the high-speed manoeuvring of HGV.…”

Section: Introductionmentioning

confidence: 99%

An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

Xue

Huang

Wei

2023

IET Radar Sonar & Navi

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The trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments.

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A fuzzy adaptive extended Kalman filter exploiting the student's t distribution for mobile robot tracking

Cited by 13 publications

References 44 publications

Application of A Grasping Autonomous Transport Robotic Car with Machine Vision in the Express Industry in China

Application of A Grasping Autonomous Transport Robotic Car with Machine Vision in the Express Industry in China

An Adaptive Fuzzy Neural Network Based On Progressive Gaussian Approximate Filter with Variable Step Size

An adaptive multivariate Student's t‐process recursive method for hypersonic glide vehicle trajectory prediction

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