Grasping Control Method of Manipulator Based on Binocular Vision Combining Target Detection and Trajectory Planning

Han, Yi; Zhao, Kai; Chu, Zhaobi; Zhou, Yan

doi:10.1109/access.2019.2954339

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…Based on the visual sensor, this algorithm can accurately calculate the pose of targets, which can guide the subsequent movement and grasping of the robot. Han et al proposed a control system of robotic manipulator grasping based on binocular visual sensors [ 17 ]. The Canny edge detection algorithm and inverse kinematics were used to solve the motion of the robotic manipulator, which improved the success rate of the robotic manipulator for grasping different targets.…”

Section: Introductionmentioning

confidence: 99%

An Ultrasonic Target Detection System Based on Piezoelectric Micromachined Ultrasonic Transducers

Gao

Tong

et al. 2023

Micromachines

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In this paper, an ultrasonic target detection system based on Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) is proposed, which consists of the PMUTs based ultrasonic sensor and the sensor system. Two pieces of 3 × 3 PMUTs arrays with the resonant frequency of 115 kHz are used as transmitter and receiver of the PMUTs-based ultrasonic sensor. Then, the sensor system can calculate the target’s position through the signal received by the above receiver. The static and dynamic performance of the proposed prototype system are characterized on black, white, and transparent targets. The experiment results demonstrated that the proposed system can detect targets of different colors, transparencies, and motion states. In the static experiments, the static location errors of the proposed system in the range of 200 mm to 320 mm are 0.51 mm, 0.50 mm and 0.53 mm, whereas the errors of a commercial laser sensor are 2.89 mm, 0.62 mm, and N\A. In the dynamic experiments, the experimental materials are the targets with thicknesses of 1 mm, 1.5 mm, 2 mm and 2.5 mm, respectively. The proposed system can detect the above targets with a maximum detection error of 4.00%. Meanwhile, the minimum resolution of the proposed system is about 0.5 mm. Finally, in the comprehensive experiments, the proposed system successfully guides a robotic manipulator to realize the detecting, grasping, and moving of a transparent target with 1 mm. This ultrasonic target detection system has demonstrated a cost-effective method to detect targets, especially transparent targets, which can be widely used in the detection and transfer of glass substrates in automated production lines.

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Section: Introductionmentioning

confidence: 99%

An Ultrasonic Target Detection System Based on Piezoelectric Micromachined Ultrasonic Transducers

Gao

Tong

et al. 2023

Micromachines

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“…Hu et al [18] established a projection mapping relationship between plane and space and proposed a 6D pipeline pose estimation method based on machine vision. Han et al [19] applied binocular vision to identify and locate targets and improved the accuracy of robotic arm grasping. Lu et al [20] applied machine vision to a sorting robot and developed a method that combined the YOLOv3 algorithm with manual features to improve sorting efficiency.…”

Section: Introductionmentioning

confidence: 99%

A New Kinect V2-Based Method for Visual Recognition and Grasping of a Yarn-Bobbin-Handling Robot

et al. 2022

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This work proposes a Kinect V2-based visual method to solve the human dependence on the yarn bobbin robot in the grabbing operation. In this new method, a Kinect V2 camera is used to produce three-dimensional (3D) yarn-bobbin point cloud data for the robot in a work scenario. After removing the noise point cloud through a proper filtering process, the M-estimator sample consensus (MSAC) algorithm is employed to find the fitting plane of the 3D cloud data; then, the principal component analysis (PCA) is adopted to roughly register the template point cloud and the yarn-bobbin point cloud to define the initial position of the yarn bobbin. Lastly, the iterative closest point (ICP) algorithm is used to achieve precise registration of the 3D cloud data to determine the precise pose of the yarn bobbin. To evaluate the performance of the proposed method, an experimental platform is developed to validate the grabbing operation of the yarn bobbin robot in different scenarios. The analysis results show that the average working time of the robot system is within 10s, and the grasping success rate is above 80%, which meets the industrial production requirements.

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“…The robot for breast ultrasound examination requires high precision and efficiency to meet the scanning requirements. Therefore, it is very important to plan the scanning path of the robots [13]- [15]. The planned scanning path can more accurately detect the diseased position.…”

Section: Introductionmentioning

confidence: 99%

Scanning Path Planning of the Robot for Breast Ultrasound Examination Based on Binocular Vision and NURBS

Zhang

et al. 2022

IEEE Access

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Ultrasonic scanning is the most commonly used method for detecting breast cancer. Traditional breast ultrasound mainly relies on manual examination, which requires doctors with rich clinical experience. The scanning path planning of the robot for breast ultrasound examination based on binocular vision and non-uniform rational B-Splines (NURBS) is proposed in this paper to solve the limitations of traditional examination methods. First, breast images are acquired by the binocular camera. The depth information of the images is obtained through stereo matching, and the 3D point cloud reconstruction of the breast model is completed. Second, the spatial index algorithm based on the K-dimension tree (Kd-tree) and uniform sampling method are selected to denoise and down-sample the point cloud model to complete the preliminary optimization of the model. The point cloud information for the scanning path is extracted according to the method of point cloud extraction and the principle of normal vector calculation. Then, the interpolation optimization of the scanning path based on the NURBS curve is completed. The optimized scanning path is transformed into the robot motion path. Finally, the robot motion is simulated and analyzed, and the experimental results show that the rationality of robot operation and the smoothness of robot motion meet the requirements of path planning defined in this paper. The error analysis experiment shows that the path planned by this method can ensure the efficiency and accuracy of ultrasound examination.INDEX TERMS Ultrasound examination, binocular vision, NURBS, scanning path planning, point cloud reconstruction, robot path planning.

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Grasping Control Method of Manipulator Based on Binocular Vision Combining Target Detection and Trajectory Planning

Cited by 14 publications

References 19 publications

An Ultrasonic Target Detection System Based on Piezoelectric Micromachined Ultrasonic Transducers

An Ultrasonic Target Detection System Based on Piezoelectric Micromachined Ultrasonic Transducers

A New Kinect V2-Based Method for Visual Recognition and Grasping of a Yarn-Bobbin-Handling Robot

Scanning Path Planning of the Robot for Breast Ultrasound Examination Based on Binocular Vision and NURBS

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