Visual Control of Robots Using Range Images

Pomares, Jorge; Gil, Pablo; Torres, Fernando

doi:10.3390/s100807303

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…In the last years, ToF cameras have been used to guide a robot arm. For example, J. Pomares et al [2] integrated the depth information in the interaction matrix for an image-based visual servoing system; while I. Siradjuddin et al [3] presented a position-based visual tracking system of a redundant manipulator, using a Kinect camera.…”

Section: Introductionmentioning

confidence: 99%

Position predictive control of an anthropomorphic robotic arm using a time-of-flight camera

Martínez

Cardenas

García

et al. 2014

IEEE SENSORS 2014 Proceedings

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This paper introduces a predictive control strategy for visual servoing an anthropomorphic robotic arm in an object tracking task. Robot workspace constraints and the target location are addressed through a time-of-flight camera. We present a methodology to process the vision information to explicitly identify and remove obstacles and objects from the point cloud. Later, this information is used by a predictive-control strategy, achieving convergence during the robotic arm motion and avoiding obstacles that are included in the cost function as constraints. The proposal validation was successfully tested on a real robotic set-up.

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Section: Introductionmentioning

confidence: 99%

Position predictive control of an anthropomorphic robotic arm using a time-of-flight camera

Martínez

Cardenas

García

et al. 2014

IEEE SENSORS 2014 Proceedings

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“…There are many ways to classify the visual servo systems in actual application [4,5]. By system structure, the visual servo systems can be divided into eye-in-hand system and eye-tohand system.…”

Section: Introductionmentioning

confidence: 99%

Visual Servo System Based on Cubature Kalman Filter and Backpropagation Neural Network

Huang¹,

Zhao²,

Zhang³

2019

I2M

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This paper attempts to enhance the estimation accuracy of image Jacobian matrix, despite the impacts from unknown statistical features of process noise and measurement noise. For this purpose, the singular value decomposition aided Cubature Kalman filter (SVDCKF) was coupled with a noise compensator for visual servo systems based on the backpropagation neural network (BPNN). The proposed algorithm improves the accuracy of the Kalman filter and compensates for the two noise covariances through the nonlinear approximation function of the BPNN. The proposed Jacobian matrix estimation algorithm was subjected to a binocular stereo visual servoing experiment on the Motoman UP6 robot experimental platform. The experimental results show that the proposed algorithm is capable of completing servo positioning.

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“…The Time of Flight (ToF) (Pomares et al, 2010) and RGB-D (Red, Green, Blue and Depth) (Teuliere and Marchand, 2012) cameras are an example. These visual sensors play an important role in the new perception context because they usually use a light source to compute the depth information.…”

Section: Introductionmentioning

confidence: 99%

Guidance of Robot Arms using Depth Data from RGB-D Camera

García¹,

Gil²,

Llácer³

et al. 2013

Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics

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Abstract:Image Based Visual Servoing (IBVS) is a robotic control scheme based on vision. This scheme uses only the visual information obtained from a camera to guide a robot from any robot pose to a desired one. However, IBVS requires the estimation of different parameters that cannot be obtained directly from the image. These parameters range from the intrinsic camera parameters (which can be obtained from a previous camera calibration), to the measured distance on the optical axis between the camera and visual features, it is the depth. This paper presents a comparative study of the performance of D-IBVS estimating the depth from three different ways using a low cost RGB-D sensor like Kinect. The visual servoing system has been developed over ROS (Robot Operating System), which is a meta-operating system for robots. The experiments prove that the computation of the depth value for each visual feature improves the system performance.

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Visual Control of Robots Using Range Images

Cited by 9 publications

References 13 publications

Position predictive control of an anthropomorphic robotic arm using a time-of-flight camera

Position predictive control of an anthropomorphic robotic arm using a time-of-flight camera

Visual Servo System Based on Cubature Kalman Filter and Backpropagation Neural Network

Guidance of Robot Arms using Depth Data from RGB-D Camera

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