An image-based visual servo control system based on an eye-in-hand monocular camera for autonomous robotic grasping

Tongloy, Teerawat; Boonsang, Siridech

doi:10.1109/ica.2016.7811489

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…In [ 6 ], a robotic system endowed with only a single camera mounted in eye-in-hand configuration was used for the ball catching task. In [ 7 ], a robot with an eye-in-hand monocular camera was used for autonomous object grasping. The methods of [ 5 , 6 , 7 ] can resolve the stated problem.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 7 ], a robot with an eye-in-hand monocular camera was used for autonomous object grasping. The methods of [ 5 , 6 , 7 ] can resolve the stated problem. The eye-in-hand concept enables the camera to maneuver along with the robotic arm, which improves the trajectory prediction precision in an open space, especially when the object is near the robot.…”

Section: Introductionmentioning

confidence: 99%

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Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot

Kao

2021

Sensors

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The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball’s position and velocity. The ball’s future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot

Kao

2021

Sensors

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“…8 At present, most of the research work on visual servoing based on feature points uses objects with simple geometric features and backgrounds as the research objects. [9][10][11][12][13][14] For instance, four black dots on a flat square part were used as image feature points, and the visual servoing of a 10-DOF articulated maintenance arm robot was simulated. 9 Four corners of a green rectangle were used as image feature points to verify the effectiveness of the IBVS control method based on the eye-in-hand monocular camera.…”

Section: Introductionmentioning

confidence: 99%

“…9 Four corners of a green rectangle were used as image feature points to verify the effectiveness of the IBVS control method based on the eye-in-hand monocular camera. 10 In order to improve the accuracy of image processing, four low-noise and the same feature shapes were carefully designed on A4 paper, which were used as image features for incremental visual servoing research. 11 There are also many literatures that selected the four corners of a black rectangular block, 12 four dots on a square object, 13 and four dots on A4 paper 14 as image features to carry out related IBVS research.…”

Section: Introductionmentioning

confidence: 99%

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Liu

Tao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, to address the problem of automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, a novel image-based visual servoing (IBVS) control method based on the corner points features of YOLOv3 object detection bounding box is proposed and applied to the robotic grasping system of bolster spring. The YOLOv3 object detection model is used to detect and position the bolster spring and then based on the corner points features of the bolster spring bounding box, the IBVS controller is designed to drive the end effector of the robot to the desired pose. This method adopts the approach-align-grasp control strategy to achieve the grasping of the bolster spring, which is very robust to the calibration parameter errors of the camera and the robot model. With the help of Robotics and Machine Vision Toolboxes in Matlab, IBVS simulation analysis based on feature points is carried out. The results show that it is reasonable to use the corner points of the object detection bounding box as image features under the initial pose where the image plane is parallel to the object plane. The positioning and grasping experiments are conducted on the robotic grasping platform of bolster spring. The results show that this method is effective for automatic positioning and grasping of bolster spring with complex geometric features and cluttered background, and it has strong robustness to the change of illumination.

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“…Additionally, some related works make use of stereo cameras to track a target, in which both cameras are placed at a predetermined distance and rotation, calculating a whole 3D reconstruction of the scene [18,19,20] using Epipolar Geometry [21]. A single-camera system can also be deployed [22] simulating a stereo system, either using markers [23], or previously establishing the separation parameters among two images [24], knowing the relationship between the key-points of both images, necessary to build the Epipolar Geometry. Nobakht and Liu [25] proposed a method to estimate the position of the camera with respect to the world, using a known object to compute the position by Epipolar Geometry.…”

Section: Introductionmentioning

confidence: 99%

Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets

Almagro

Castro

Lunghi

et al. 2019

Sensors

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Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot using manual control in critical steps, as well as semi-autonomous behaviours in more secure scenarios, by using, for example, object tracking and recognition techniques. This paper describes a novel vision system to track and estimate the depth of metallic targets for robotic interventions. The system has been designed for on-hand monocular cameras, focusing on solving lack of visibility and partial occlusions. This solution has been validated during real interventions at the Centre for Nuclear Research (CERN) accelerator facilities, achieving 95% success in autonomous mode and 100% in a supervised manner. The system increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases. The integration of such an assistance system is especially important when facing complex (or repetitive) tasks, in order to reduce the work load and accumulated stress of the operator, enhancing the performance and safety of the mission.

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An image-based visual servo control system based on an eye-in-hand monocular camera for autonomous robotic grasping

Cited by 8 publications

References 9 publications

Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot

Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets

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