Binocular visual tracking and grasping of a moving object with a 3D trajectory

Fuentes-Pacheco, Jorge; Ruiz-Ascencio, José; Rendón-Mancha, Juan Manuel

doi:10.22201/icat.16656423.2009.7.03.486

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…They have the advantage of being cost effective since the cost of image processing has reduced, self-contained systems and also their ability for providing relevant information on the status of robots and their immediate physical environment. The significance of having visual information stands out in applications with unstructured or changing environments [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

3D Visual Tracking of an Articulated Robot in Precision Automated Tasks

Alzarok

Fletcher

Longstaff

2017

Sensors

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The most compelling requirements for visual tracking systems are a high detection accuracy and an adequate processing speed. However, the combination between the two requirements in real world applications is very challenging due to the fact that more accurate tracking tasks often require longer processing times, while quicker responses for the tracking system are more prone to errors, therefore a trade-off between accuracy and speed, and vice versa is required. This paper aims to achieve the two requirements together by implementing an accurate and time efficient tracking system. In this paper, an eye-to-hand visual system that has the ability to automatically track a moving target is introduced. An enhanced Circular Hough Transform (CHT) is employed for estimating the trajectory of a spherical target in three dimensions, the colour feature of the target was carefully selected by using a new colour selection process, the process relies on the use of a colour segmentation method (Delta E) with the CHT algorithm for finding the proper colour of the tracked target, the target was attached to the six degree of freedom (DOF) robot end-effector that performs a pick-and-place task. A cooperation of two Eye-to Hand cameras with their image Averaging filters are used for obtaining clear and steady images. This paper also examines a new technique for generating and controlling the observation search window in order to increase the computational speed of the tracking system, the techniques is named Controllable Region of interest based on Circular Hough Transform (CRCHT). Moreover, a new mathematical formula is introduced for updating the depth information of the vision system during the object tracking process. For more reliable and accurate tracking, a simplex optimization technique was employed for the calculation of the parameters for camera to robotic transformation matrix. The results obtained show the applicability of the proposed approach to track the moving robot with an overall tracking error of 0.25 mm. Also, the effectiveness of CRCHT technique in saving up to 60% of the overall time required for image processing.

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Section: Introductionmentioning

confidence: 99%

3D Visual Tracking of an Articulated Robot in Precision Automated Tasks

Alzarok

Fletcher

Longstaff

2017

Sensors

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“…In a learning stage, sets of pixels whose intensities allow for optimal prediction of the transformations are selected as a support for the linear predictor. [16] presents a binocular eye-to-hand visual servoing system that is able to track and grasp a moving object in real time. In the tracking module, they use three linear predictors (one for each component of the three dimensions) to predict and generate the trajectory that will describe the 3D object position in the near future, therefore, their manipulator robot is able to track and grasp a moving object, even if the object is temporarily occluded.…”

Section: Introductionmentioning

confidence: 99%

Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object without Cameras

Chaabaani¹

2015

IJIEE

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“…The 3D shape measurement and reconstruction has become one of the hottest fields in computer vision and robotics during the past few years. Researchers from various fields like computer vision, robotics, mechatronics, intelligent manufacturing systems and applied optics have worked enormously to find more robust, less complex and faster techniques [1,2]. These techniques are being adapted for medical, rapid prototyping, defense and other numerous industries.…”

Section: Introductionmentioning

confidence: 99%

Projector Calibration for Pattern Projection Systems

Irfanud¹,

Anwar²,

Syed³

et al. 2014

Journal of Applied Research and Technology

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In this paper we proposed a method for geometric calibration of a projector. This method makes use of a calibrated camera to calibrate the projector. Since the projector works inversely with a camera i.e., it projects the image instead of capturing it, so it can be considered as a reverse camera. The projector is calibrated with the help of a calibrated camera using two types of chessboard, a printed chessboard and a projected chessboard by the projector. The object points of the projected chessboard pattern are measured with the help of calibrated camera and the image points are directly acquired from the chessboard pattern to be projected. Then using these object points and image points the projector is calibrated. Once the projector calibration is done, the transformation matrices (from projector to screen, from camera to screen and from camera to projector) are determined which are used for the reconstruction of the 3D geometry.

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Binocular visual tracking and grasping of a moving object with a 3D trajectory

Cited by 10 publications

References 11 publications

3D Visual Tracking of an Articulated Robot in Precision Automated Tasks

3D Visual Tracking of an Articulated Robot in Precision Automated Tasks

Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object without Cameras

Projector Calibration for Pattern Projection Systems

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