Vision-based control of UR5 robot to track a moving object under occlusion using Adaptive Kalman Filter

doi:10.1145/3352593.3352675

Proceedings Article•DOI•

Vision-based control of UR5 robot to track a moving object under occlusion using Adaptive Kalman Filter

K. Ramachandruni¹, S. Jaiswal¹, Suril V. Shah¹•Institutions (1)

Indian Institute of Technology, Jodhpur¹

02 Jul 2019-

TL;DR: A robust method to track a moving object under occlusion using an off-the-shelf monocular camera and a 6 Degree of Freedom (DOF) articulated arm and an Adaptive Kalman Filter to improve the visual feedback of the camera is presented.

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Abstract: This paper presents a robust method to track a moving object under occlusion using an off-the-shelf monocular camera and a 6 Degree of Freedom (DOF) articulated arm. The visual servoing problem of tracking a known object using data from a monocular camera can be solved with a simple closed loop controller. However, this system frequently fails in situations where the object cannot be detected and to overcome this problem an estimation based tracking system is required. This work employs an Adaptive Kalman Filter (AKF) to improve the visual feedback of the camera. The role of the AKF is to estimate the position of the object when it is occluded/out of view and remove the noise and uncertainties associated with visual data. Two estimation models for the AKF are selected for comparison and among them, the Mean-Adaptive acceleration model is implemented on a 6-DOF UR5 articulated arm with a monocular camera mounted in eye-in-hand configuration to follow the known object in 2D cartesian space (without using depth information).

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Journal Article•DOI•

Survey of maneuvering target tracking. Part I. Dynamic models

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X. Rong Li, Vesselin P. Jilkov

01 Oct 2003-IEEE Transactions on Aerospace and Electronic Systems

TL;DR: A comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the measurement-origin uncertainty is presented in this article, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion.

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Abstract: This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere.

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1,897 citations

"Vision-based control of UR5 robot t..." refers background or methods or result in this paper

...Though this update rule is similar to the acceleration update suggested in [4] (i....
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...As a modification to the mean acceleration update rule proposed in [4], in the current implementation an exponential moving average (EMA, [10]) was taken instead:...
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...Thismodel was proposed in [4] as one of a number of dynamic models suitable for tracking maneuvering targets....
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...Other mathematical models were presented in [4] to use for tracking moving targets....
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Journal Article•DOI•

Video object tracking using adaptive Kalman filter

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Shiuh-Ku Weng¹, Chung-Ming Kuo², Shu-Kang Tu²•Institutions (2)

United States Naval Academy¹, I-Shou University²

01 Dec 2006-Journal of Visual Communication and Image Representation

TL;DR: The proposed method has the robust ability to track theMoving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the movingobject, and changing the velocity of moving object suddenly.

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314 citations

"Vision-based control of UR5 robot t..." refers background or methods in this paper

...[2] uses a constant velocity model to model the AKF while [3] uses a zero mean acceleration model known as Singer model....
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...Other papers such as [2] and [3] use Adaptive Kalman filters (AKFs), where the estimation parameters are adjusted according to the current motion....
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...By studying the various applications for which similar models have been used([2] and [3]), it can be suggested that the Uniform Velocity model works better for applications where the acceleration of motion is very low i....
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...The occlusion rate can be used as a measure of noise covariance as described in [2], where the measurement error is considered directly proportional to the occlusion rate (more occlusion leads to less accurate measurement)....
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...As explained in [2], the occlusion rate is the ratio of occlusion area (in pixels) in frame t to that in frame t − 1....
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DOI•

Kalman filter for vision tracking

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Erik Cuevas, Daniel Zaldivar, Raúl Rojas¹•Institutions (1)

Free University of Berlin¹

01 Jan 2005

TL;DR: The capacity of the Kalman Filter to allow small occlusions and also the use of the extended Kalman filter (EKF) to model complex movements of objects are considered.

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Abstract: The Kalman filter has been used successfully in different prediction applications or state determination of a system. One important field in computer vision is the object tracking. Different movement conditions and occlusions can hinder the vision tracking of an object. In this report we present the use of the Kalman filter in the vision tracking. We consider the capacity of the Kalman filter to allow small occlusions and also the use of the extended Kalman filter (EKF) to model complex movements of objects.

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174 citations

"Vision-based control of UR5 robot t..." refers methods in this paper

...[1] used an Extended Kalman filter (Kalman filter for non-linear models) with unconstrained brownian motion model for object tracking from visual data....
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Journal Article•DOI•

Image-Based Visual Servoing of a 7-DOF Robot Manipulator Using an Adaptive Distributed Fuzzy PD Controller

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Indrazno Siradjuddin¹, Laxmidhar Behera¹, T. Martin McGinnity¹, Sonya Coleman¹•Institutions (1)

Ulster University¹

01 Apr 2014-IEEE-ASME Transactions on Mechatronics

TL;DR: It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller.

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Abstract: This paper is concerned with the design and implementation of a distributed proportional-derivative (PD) controller of a 7-degrees of freedom (DOF) robot manipulator using the Takagi-Sugeno (T-S) fuzzy framework. Existing machine learning approaches to visual servoing involve system identification of image and kinematic Jacobians. In contrast, the proposed approach actuates a control signal primarily as a function of the error and derivative of the error in the desired visual feature space. This approach leads to a significant reduction in the computational burden as compared to model-based approaches, as well as existing learning approaches to model inverse kinematics. The simplicity of the controller structure will make it attractive in industrial implementations where PD/PID type schemes are in common use. While the initial values of PD gain are learned with the help of model-based controller, an online adaptation scheme has been proposed that is capable of compensating for local uncertainties associated with the system and its environment. Rigorous experiments have been performed to show that visual servoing tasks such as reaching a static target and tracking of a moving target can be achieved using the proposed distributed PD controller. It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller. The proposed control scheme is applied and assessed in real-time experiments using an uncalibrated eye-in-hand robotic system with a 7-DOF PowerCube robot manipulator.

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77 citations

"Vision-based control of UR5 robot t..." refers background in this paper

...The final PD control law can be written as ([7], [8]):...
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Book Chapter•DOI•

Moving Averages for Financial Data Smoothing

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Aistis Raudys¹, Vaidotas Lenčiauskas¹, Edmundas Malčius¹•Institutions (1)

Vilnius University¹

10 Oct 2013

TL;DR: This large-scale empirical study performed on 1850 real-world time series including stocks, ETF, Forex and futures daily data demonstrate that the best smoothness/lag ratio is achieved by the Exponential Hull Moving Average and Triple Exponential Moving Average.

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Abstract: For a long time moving averages has been used for a financial data smoothing. It is one of the first indicators in technical analysis trading. Many traders debated that one moving average is better than other. As a result a lot of moving averages have been created. In this empirical study we overview 19 most popular moving averages, create a taxonomy and compare them using two most important factors – smoothness and lag. Smoothness indicates how much an indicator change (angle) and lag indicates how much moving average is lagging behind the current price. The aim is to have values as smooth as possible to avoid erroneous trades and with minimal lag – to increase trend detection speed. This large-scale empirical study performed on 1850 real-world time series including stocks, ETF, Forex and futures daily data demonstrate that the best smoothness/lag ratio is achieved by the Exponential Hull Moving Average (with price correction) and Triple Exponential Moving Average (without correction).

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33 citations

"Vision-based control of UR5 robot t..." refers methods in this paper

...As a modification to the mean acceleration update rule proposed in [4], in the current implementation an exponential moving average (EMA, [10]) was taken instead:...
[...]

Vision-based control of UR5 robot to track a moving object under occlusion using Adaptive Kalman Filter

References

"Vision-based control of UR5 robot t..." refers background or methods or result in this paper

"Vision-based control of UR5 robot t..." refers background or methods in this paper

"Vision-based control of UR5 robot t..." refers methods in this paper

"Vision-based control of UR5 robot t..." refers background in this paper

"Vision-based control of UR5 robot t..." refers methods in this paper

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