Trajectory Tracking Control for Flexible-Joint Robot Based on Extended Kalman Filter and PD Control

Ma, Tianyu; Song, Zhibin; Xiang, Zhongxia; Dai, Jian S.

doi:10.3389/fnbot.2019.00025

Cited by 6 publications

(3 citation statements)

References 28 publications

(26 reference statements)

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“…In this vein, all variants of the KF have been combined with classic control techniques like PID (Proportional-Integral-Derivative controller). In a study conducted by [63], the design of a control structure for a robot arm with flexible joints is presented. The authors propose closed-loop control with a Proportional Derivative (PD) structure and the EKF as a state estimator.…”

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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“…The most used methodology of estimation and fusion of sensors is the Extended Kalman Filter (EKF), due to its statistical properties, in addition to the versatility for different dynamic models and sensor set [12,13]. This filter has two different steps, prediction and update.…”

Section: Introductionmentioning

confidence: 99%

An Embedded Quaternion-Based Extended Kalman Filter Pose Estimation for Six Degrees of Freedom Systems

Medeiros

Pimentel

Garibotti

2021

J Intell Robot Syst

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This paper proposes a formulation of quaternion-based Extended Kalman Filter pose estimation for six degrees of freedom systems embedded in an FPGA with commercial processors. Our approach uses the fusion of a camera and an inertial measurement unit to estimate simultaneously the position and the orientation of the system of interest. In addition, a Stewart platform is used to validate and evaluate the estimated pose. Although this work considers the use of common low-cost sensors and the use of markers with simple geometry, the results show excellent performance of the developed filter, being able to estimate the pose and orientation with an error below 8.14 mm and 0.63 o ¯, respectively. Furthermore, the effectiveness of the approach has also been evaluated, showing that the filter is able to converge quickly when the markers are retrieved after a loss of camera data for a short period of time.

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“…EKF linearizes the system under consideration around the operating point and then feeds to the KF. EKF has widely been used in robotics [1,10,11] and unmanned aerial vehicle [12]. However, EKF is inherently limited by its computational complexity and long execution time originating from the linearization step.…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust Localization of Surgical Array using Kalman Filter

Ashikuzzaman¹,

Jafarpisheh²,

Rottoo³

et al. 2020

Preprint

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Intraoperative tracking of surgical instruments is an inevitable task of computer-assisted surgery. An optical tracking system often fails to precisely reconstruct the dynamic location and pose of a surgical tool due to the acquisition noise and measurement variance. Embedding a Kalman Filter (KF) or any of its extensions such as extended and unscented Kalman filters with the optical tracker resolves this issue by reducing the estimation variance and regularizing the temporal behavior. However, the current rigid-body KF implementations are computationally burdensome and hence, takes long execution time which hinders real-time surgical tracking. This paper introduces a fast and computationally efficient implementation of linear KF to improve the measurement accuracy of an optical tracking system with high temporal resolution. Instead of the surgical tool as a whole, our KF framework tracks each individual fiducial mounted on it using a Newtonian model. In addition to simulated dataset, we validate our technique against real data obtained from a high frame-rate commercial optical tracking system. The proposed KF framework substantially stabilizes the tracking behavior in all of our experiments and reduces the mean-squared error (MSE) from the order of 10 −2 mm 2 to 10 −4 mm 2 .

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Trajectory Tracking Control for Flexible-Joint Robot Based on Extended Kalman Filter and PD Control

Cited by 6 publications

References 28 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

An Embedded Quaternion-Based Extended Kalman Filter Pose Estimation for Six Degrees of Freedom Systems

Fast and Robust Localization of Surgical Array using Kalman Filter

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