Multiple sensor fusion for mobile robot localization and navigation using the Extended Kalman Filter

Khatib, Ehab I. Al; Jaradat, Mohammad A.; Abdel–Hafez, Mamoun F.; Roigari, Milad

doi:10.1109/isma.2015.7373480

Cited by 27 publications

(14 citation statements)

References 15 publications

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“…An interesting approach on the use of different sensors to obtain the position and location of a mobile robot in a specific space, as well as their destination, is conducted in [57]. In this study, a mobile robot is equipped with several types of sensors such as encoder, GNSS, gyroscopes, compasses, and accelerometers.…”

Section: Applications Of the Kalman Filter In The Robotics Fieldmentioning

confidence: 99%

Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Urrea

Agramonte

2021

Journal of Sensors

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Due to its widespread application in the robotics field, the Kalman filter has received increased attention from researchers. This work reviews some of the modifications conducted on to this algorithm over the last years. Problems such as the consistency, convergence, and accuracy of the filter are also dealt with. Sixty years after its creation, the Kalman filter is still used in autonomous navigation processes, robot control, and trajectory tracking, among other activities. The filter is not only restricted to robotics but is also present in different fields, such as economics and medicine. In addition, the characteristics of each modification on this filter are analyzed and compared.

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Section: Applications Of the Kalman Filter In The Robotics Fieldmentioning

confidence: 99%

Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Urrea

Agramonte

2021

Journal of Sensors

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“…Akbar Assa et al propose an enhanced Kalman filtering framework for sensor fusion that provides robustness against uncertainty in system parameters [21]. Ehab I. Al-Khatib et al applied an EKF to the fusion of multi-sensor data for robot localization [22].…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

et al. 2021

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Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four different electrical loadings, and a bi-directional long short-term memory (BiLSTM) deep neural network is trained for future predictions. The proposed model provides a unique approach and accuracy in SMPS fault indication with the aid of electrical parameters.

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“…For differential AGV, Luo et al proposed a fuzzy PID algorithm based on the optimal deviation path [1], which can correct the tracking accuracy of 3.2 mm and the lateral deviation within 5 mm, but the acquisition method of position and attitude deviation affects the robustness of the system. Al-khatib et al used extended Kalman algorithm and proved that all the fused positioning information is more accurate by comparing the experimental results [2]. Yang et al used model predictive control method to build dynamic trajectory planning model, which can easily meet various physical constraints, solve the problem of limited computing power of processor, and improve the robustness of closed-loop tracking system [3].…”

Section: Related Workmentioning

confidence: 99%

Trajectory tracking control of AGV based on time-varying state feedback

Shang

Zhang

2021

J Wireless Com Network

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In order to realize the automation of transport equipment, according to the automatic guided vehicle (AGV) kinematics model, the trajectory tracking control of AGV with front wheel steering and rear wheel drive is studied. Lyapunov function is used to ensure the global stability of the system. Time-varying state feedback method based on integral backstepping is adopted, introducing virtual feedback, a trajectory tracking controller for AGV with nonholonomic constraints is proposed. In order to ensure the smooth motion of AGV, the speed and acceleration limited strategy of AGV is introduced into the controller. Simulation results show that the algorithm is fast, accurate and globally stable for different paths.

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Multiple sensor fusion for mobile robot localization and navigation using the Extended Kalman Filter

Cited by 27 publications

References 15 publications

Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

RUL Prediction of Switched Mode Power Supply Using a Kalman Filter Assisted Deep Neural Network

Trajectory tracking control of AGV based on time-varying state feedback

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