Intelligent multi-sensor fusion techniques in flexible manufacturing workcells

Kumar, Manish; Garg, Devendra P.

doi:10.23919/acc.2004.1384707

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…The Kalman Filter is used to estimate the states of a dynamic system described by a linear stochastic difference equation and is extensively used in tracking and estimation problems [16], [17]. An Extended Kalman filter finds application in the cases where the system process is non-linear such as the problem discussed in this research.…”

Section: Extended Kalman Filter For Determining the Position Coordinatesmentioning

confidence: 99%

Inverse Kinematics and Extended Kalman Filter Based Motion Tracking of Human Limb

Isaac

Kumar

Huang

et al. 2014

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Sys

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This paper addresses the problem of motion tracking of human skeleton system using non-invasive vision based sensors. The proposed approach combines synergistic paradigms of image processing, kinematics of rigid bodies and Extended Kalman Filtering scheme to estimate the motion of a human limb system. This approach solely depends on the measurement obtained from the vision sensors without involving any wearable or inertial sensors to measure the motion parameters. In this paper we propose fusion of two filtering schemes — the optical flow equations applied to raw images obtained from the Microsoft Kinect and extended Kalman filter for human skeleton considered as a system of kinematic linkages. The strategy proposed in this paper yields near optimal results as is demonstrated with the help of experiments performed using the Microsoft Kinect sensor and compared using accurate tracks obtained from 24-Camera Optitrack motion capture system.

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Section: Extended Kalman Filter For Determining the Position Coordinatesmentioning

confidence: 99%

Inverse Kinematics and Extended Kalman Filter Based Motion Tracking of Human Limb

Isaac

Kumar

Huang

et al. 2014

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Sys

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“…Sensor modeling [22][23][24][25] forms an important part of sensor fusion and it deals with developing an understanding of the nature of measurements provided by the sensor, the limitations of the sensor, and probabilistic understanding of the sensor performance in terms of the uncertainties. The information supplied by a sensor is usually modeled as a mean about a true value, with uncertainty due to noise represented by a variance that depends on both the measured quantities themselves and the operational parameters of the sensor.…”

Section: Bayesian Approach For Sensor Fusionmentioning

confidence: 99%

Stochastic adaptive sensor modeling and data fusion

Kumar

Garg

Zachery³

2006

SPIE Proceedings

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One of the major problems in sensor fusion is that sensors frequently provide spurious observations which are difficult to predict and model. The spurious data from sensors must be identified and eliminated from the sensor fusion since its incorporation in the fusion pool might lead to inaccurate estimation. This paper presents a sensor fusion strategy based on Bayesian approach that can automatically identify the inconsistency in sensor data so that the spurious sensor data can be eliminated from the sensor fusion process. The proposed method adds a term to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state. This term has the effect of increasing the variance of the posterior distribution when data from one of the sensors is inconsistent with respect to the other. The proposed strategy was verified with the help of extensive computations performed on simulated data from three sensors. A comparison was made between two different fusion schemes: centralized fusion in which data from all sensors were fused at once, and decentralized or sequential Bayesian method which provided opportunity to identify and eliminate spurious data from the fusion process. The simulations verified that the proposed strategy was able to identify spurious data, and its elimination from the fusion process led to better estimation.

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“…While industrial robots were originally developed to free workers from the monotony of repetitive work and hazardous environments, in recent years, they have been introduced in factories and companies with the primary aim of improving productivity and quality [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Xie

2022

Computational Intelligence and Neuroscience

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At present, most of the handling industrial robots working on the production line are operated by teaching or preprogramming, which makes the flexibility of the production line poor and does not meet the flexible production requirements of the material handling system. This study proposes a solution based on adding computer binocular vision to a five-axis industrial robot system. A simple and high-precision binocular camera calibration method is proposed, the kinematics of the five-axis robot is analyzed, and the target positioning is realized; the communication between the upper and lower robots is realized through Ethernet. According to the specific target, the grasping scheme of the gripper was designed; the control software was developed using two schemes. Visual control is carried out by operating specific buttons on the control panel, and visual control is carried out by executing the macrovariable program, finally realizing the joint fusion of multisensor data and binocular vision.

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Intelligent multi-sensor fusion techniques in flexible manufacturing workcells

Cited by 10 publications

References 15 publications

Inverse Kinematics and Extended Kalman Filter Based Motion Tracking of Human Limb

Inverse Kinematics and Extended Kalman Filter Based Motion Tracking of Human Limb

Stochastic adaptive sensor modeling and data fusion

Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

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