Robotic eye-to-hand coordination: Implementing visual perception to object manipulation

Jafari, Shahram; Jarvis, Ray

doi:10.3233/his-2005-2404

Cited by 8 publications

(4 citation statements)

References 51 publications

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“…The rotation matrix R is written as R = R ( ). (8) However, the relationship between the robot planar translation t and the 2D translation (Δ , Δ ) is not straightforward as in the rotation case. The robot planar translation is in essence the transformation of the 2D translation in image coordinate frame into the world coordinate frame.…”

Section: The Camera Ego-motion From Image Registrationmentioning

confidence: 99%

“…As a result, the robot camera position and orientation (pose) with respect to the table plane is known. This can be accomplished in many ways, such as touching the table plane using a tactile sensor [8]; thereby, the table plane distance from the camera can be obtained. Moreover, many plane detection methods can be used, for example [11].…”

Section: Problem Descriptionmentioning

confidence: 99%

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Camera Ego-Motion Estimation Using Phase Correlation under Planar Motion Constraint

Effendi

Jarvis

2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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An intelligent robotic living assistive system has become a popular research in the last decade. One of the important topics in that research area is 3D object reconstruction from multiple views. This process may depend on motion estimation using vision. However, often a domestic robot on an electric wheel chair has to move in a steep rotational angle that causes motion estimation from vision to become inaccurate. In addition, an oblique viewing angle creates a perspective distortion to the captured images, which further worsens the estimation result. Hence, in this paper, we propose a new approach by altering the motion estimation problem into a 2D image registration problem. Our method's accuracy is very close to that of the Scale Invariant Feature Transform (SIFT) features tracker, whereas the Kanade-Lucas-Tomasi (KLT) tracker's drops as soon as the rotational angle reaches about 40 ∘ . Although our method is 2.7 times slower than the KLT tracker, it is 19 times faster than the SIFT tracker.

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Section: The Camera Ego-motion From Image Registrationmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Camera Ego-Motion Estimation Using Phase Correlation under Planar Motion Constraint

Effendi

Jarvis

2010

2010 International Conference on Digital Image Computing: Techniques and Applications

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“…In ordinary applications, cameras are fixed and the object moves. 25 Consequently, 3D tracking of an object is done based on fixed cameras assumption. However, the situation is vice versa.…”

Section: Stereovisionmentioning

confidence: 99%

A novel approach for humanoid push recovery using stereopsis

2013

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SUMMARYPush recovery is one of the most challenging problems for the current humanoid robots. The importance of push recovery can be well observed in the real environment. The critical issue for a humanoid is to maintain and recover its balance against any disturbances. In this research a new stereovision approach is proposed to estimate the robot deviation angle and consequently, the movement of center of mass of the robot is calculated. Then, two novel strategies have been devised to recover the balance of the humanoid which are called “knee strategy” and “knee-hip strategy.” Also, a mathematical model validates the efficiency of the proposed strategies as demonstrated in the paper. Experiments have been conducted on a humanoid robot and demonstrate that the predicted robot deviation angle, using stereovision technique, converges to the actual deviation angle. Stable regions of proposed strategies illustrate that the humanoid can recover its stability in a robust manner. Vision-based estimation also shows a higher correlation to actual deviation angle and a lower fluctuation compared with the output of the acceleration sensor.

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“…[16] NNs are also widely utilized in visual servoing applications for scene analysis, object classification, pattern recognition, etc., as a front-end controller. [17,18] In some visual servoing tasks, NNs are applied in robot control to deal with the uncertainties in kinematics, dynamics, Jacobian matrices, and model uncertainties during object manipulation. [19][20][21][22][23] Kelly et al [24] deliberated visual servoing of a robot manipulator in a fixed-camera structure in which the NNs estimate the inverse perceptual kinematic mapping.…”

Section: Introductionmentioning

confidence: 99%

Neural network‐assisted robotic vision system for industrial applications

Krishnan

Ashok

2021

Asian Journal of Control

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This paper investigates the performance of neural networks in positioning an industrial robot manipulator based on image feedback. Visual servoing regulates the pose of the robot manipulator in accordance with the target image data. As the servoing proceeds, the end-effector positions to its final pose as the image feature error exponentially decreases to zero. In this paper, the trained network moves the robot manipulator from an arbitrary initial pose to an intermediate posture based on the reference image features given. Then, fine-tuning based on the traditional visual servoing method is performed to achieve an accurate pick-and-place task with minimum iterations. The experimental results prove the capability of the neural network architectures to predict the desired pose using local image descriptor, corner points. This paper investigates the performance of two neural network designs applied to visual positioning and compares them with the traditional image-based visual servoing (IBVS) method in terms of execution time.

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Robotic eye-to-hand coordination: Implementing visual perception to object manipulation

Cited by 8 publications

References 51 publications

Camera Ego-Motion Estimation Using Phase Correlation under Planar Motion Constraint

Camera Ego-Motion Estimation Using Phase Correlation under Planar Motion Constraint

A novel approach for humanoid push recovery using stereopsis

Neural network‐assisted robotic vision system for industrial applications

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