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Journal articles on the topic 'Intelligent Grasping'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

Wang, Zhongyu, Shaobo Li, Qiang Bai, Qisong Song, Xingxing Zhang, and Ruiqiang Pu. "Research on Intelligent Robot Point Cloud Grasping in Internet of Things." Micromachines 13, no. 11 (2022): 1999. http://dx.doi.org/10.3390/mi13111999.

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The development of Internet of Things (IoT) technology has enabled intelligent robots to have more sensing and decision-making capabilities, broadening the application areas of robots. Grasping operation is one of the basic tasks of intelligent robots, and vision-based robot grasping technology can enable robots to perform dexterous grasping. Compared with 2D images, 3D point clouds based on objects can generate more reasonable and stable grasping poses. In this paper, we propose a new algorithm structure based on the PointNet network to process object point cloud information. First, we use th

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2

Li, Tong, Xuguang Sun, Xin Shu, et al. "Robot Grasping System and Grasp Stability Prediction Based on Flexible Tactile Sensor Array." Machines 9, no. 6 (2021): 119. http://dx.doi.org/10.3390/machines9060119.

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As an essential perceptual device, the tactile sensor can efficiently improve robot intelligence by providing contact force perception to develop algorithms based on contact force feedback. However, current tactile grasping technology lacks high-performance sensors and high-precision grasping prediction models, which limits its broad application. Herein, an intelligent robot grasping system that combines a highly sensitive tactile sensor array was constructed. A dataset that can reflect the grasping contact force of various objects was set up by multiple grasping operation feedback from a tact

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Zelenak, Andy, Cheryl Brabec, Jack Thompson, Joey Hashem, Benito Fernandez, and Mitch Pryor. "Intelligent Grasping with the Robotic Opposable Thumb." Applied Artificial Intelligence 28, no. 8 (2014): 737–50. http://dx.doi.org/10.1080/08839514.2014.952917.

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4

Zhou, Yuliang, Mingxuan Chen, Guanglong Du, Ping Zhang, and Xin Liu. "Intelligent grasping with natural human-robot interaction." Industrial Robot: An International Journal 45, no. 1 (2018): 44–53. http://dx.doi.org/10.1108/ir-05-2017-0089.

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Purpose The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct. Design/methodology/approach First, the authors leverage Kinect to collect the environment information including both image and voice. The target object is located and segmented by gesture recognition and speech analysis and finally grasped through path teaching. To obtain the posture of the human gesture accurately, the authors use the Kalman filtering (KF) algorithm to calibrate the posture use the Gaussian mixture model (GMM) for human motion modelin

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Huang, Shiuh-Jer, Wei-Han Chang, and Jui-Yiao Su. "Intelligent robotic gripper with adaptive grasping force." International Journal of Control, Automation and Systems 15, no. 5 (2017): 2272–82. http://dx.doi.org/10.1007/s12555-016-0249-6.

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Hu, Jie, Qin Li, and Qiang Bai. "Research on Robot Grasping Based on Deep Learning for Real-Life Scenarios." Micromachines 14, no. 7 (2023): 1392. http://dx.doi.org/10.3390/mi14071392.

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The application of robots, especially robotic arms, has been primarily focused on the industrial sector due to their relatively low level of intelligence. However, the rapid development of deep learning has provided a powerful tool for conducting research on highly intelligent robots, thereby offering tremendous potential for the application of robotic arms in daily life scenarios. This paper investigates multi-object grasping in real-life scenarios. We first analyzed and improved the structural advantages and disadvantages of convolutional neural networks and residual networks from a theoreti

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Prakash, J., and M. Ilangkumaran. "An investigation of various actuation mechanisms in robot arm." Measurement and Control 52, no. 9-10 (2019): 1299–307. http://dx.doi.org/10.1177/0020294019866854.

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Many research activities have been carried out to develop a simple mechanism for grasping irregular object shapes using two- or three-fingered robot end effectors. The idea behind this work is to develop three-fingered intelligent grippers that are capable of sensing different factors like weight, effort required, compactness, robustness, and stability of the object held during the manipulations. In this paper, five different actuation mechanisms, namely, edge-cam-operated actuation mechanism, toggle-linkage-based actuation mechanism, wedge-cam-operated gripper, sliding slotted pin–ball joint

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Asheber, W. T., and Chyi Yeu Lin. "Mechatronics Design of Intelligent Robotic Gripper." Key Engineering Materials 649 (June 2015): 14–21. http://dx.doi.org/10.4028/www.scientific.net/kem.649.14.

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This work presents multi-functional robot arm gripper design along with vision and tactile sensor for efficient grasping and manipulation tasks. The design emulates human’s hand fingers structure using linkages and direct drive through slider-crank mechanism transmission. The structural elements are optimized for a finest performance in motion and force transmissibility of the gripper fingers. The main future of this design is its reliability to grasp and manipulate unknown object while its system complexity is reduced. The gripper has a tool change fixture incorporated into its palm, which wi

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9

Bowers, D. L., and R. Lumia. "Manipulation of unmodeled objects using intelligent grasping schemes." IEEE Transactions on Fuzzy Systems 11, no. 3 (2003): 320–29. http://dx.doi.org/10.1109/tfuzz.2003.812689.

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10

Jie Yu, Jie Yu, Xi-Lin Li Jie Yu, Cai-Wen Niu Xi-Lin Li, Yu-Xin Zhang Cai-Wen Niu, and Shu-Hui Xu Yu-Xin Zhang. "Research on Intelligent Assembly Strategy and Workpiece Grasping Method for Industrial Robots Based on Deep Learning." 電腦學刊 34, no. 3 (2023): 315–24. http://dx.doi.org/10.53106/199115992023063403023.

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<p>In response to the current situation of low assembly accuracy and unreasonable workpiece grasping posture in the automatic assembly process of equipment manufacturing based on industrial robots, an objective function was designed with the goal of minimizing robot grasping torque, and a deep learning strategy was used to autonomously identify the optimal grasping posture. In terms of assembly strategy selection, the assembly behavior is abstracted as the coordination between holes and shafts. A method of changing the center distance of shaft hole parts to change the jamming state of ho

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11

Zhu, Lingfeng, Yancheng Wang, Deqing Mei, and Chengpeng Jiang. "Development of Fully Flexible Tactile Pressure Sensor with Bilayer Interlaced Bumps for Robotic Grasping Applications." Micromachines 11, no. 8 (2020): 770. http://dx.doi.org/10.3390/mi11080770.

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Flexible tactile sensors have been utilized in intelligent robotics for human-machine interaction and healthcare monitoring. The relatively low flexibility, unbalanced sensitivity and sensing range of the tactile sensors are hindering the accurate tactile information perception during robotic hand grasping of different objects. This paper developed a fully flexible tactile pressure sensor, using the flexible graphene and silver composites as the sensing element and stretchable electrodes, respectively. As for the structural design of the tactile sensor, the proposed bilayer interlaced bumps ca

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12

Li, Min Shuo, and Ming Hai Yao. "Cognitive Developmental Grasp Robots: From Infant Development to Computational Modeling." Advanced Materials Research 706-708 (June 2013): 682–86. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.682.

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We addressed the idea that a hybrid of symbolic and sub-symbolic representations is the best method to represent cognition for developmental robots. Basing the learning mechanisms of a cognitive developmental robot on principles of human-like learning, we developed an intelligent grasping robot architecture based on the hierarchical learning. The hybrid of different representations and memory in IGR also gave a new direction for developing robots with high intelligence.

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13

Zhendong, Shang, Zhang Xiaolan, and Zhou Yuting. "Realization of Intelligent Grasping on a Traditional Hydraulic Manipulator." Open Automation and Control Systems Journal 7, no. 1 (2015): 1949–57. http://dx.doi.org/10.2174/1874444301507011949.

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14

Ding, Weiliang, Gongfa Li, Guozhang Jiang, Yinfeng Fang, Zhaojie Ju, and Honghai Liu. "Intelligent Computation in Grasping Control of Dexterous Robot Hand." Journal of Computational and Theoretical Nanoscience 12, no. 12 (2015): 6096–99. http://dx.doi.org/10.1166/jctn.2015.4642.

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15

Huang, Shiuh Jer, Wei Han Chang, Jui Yiao Su, and Yan Chen Liu. "Distributed Control Intelligent Robotic Gripper." Applied Mechanics and Materials 479-480 (December 2013): 742–46. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.742.

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An intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of grasped object mass and soft/hard features. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown by pictures to verify this distributed system performance. The position and force tracking errors are less than 1 mm and 0.1 N, respectively.

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16

Daqi, Jiang, Wang Hong, Zhou Bin, and Wei Chunfeng. "An industrial intelligent grasping system based on convolutional neural network." Assembly Automation 42, no. 2 (2022): 236–47. http://dx.doi.org/10.1108/aa-03-2021-0036.

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Purpose This paper aims to save time spent on manufacturing the data set and make the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level. Design/Methodology/Approach The proposed system comprises two diverse kinds of convolutional neuron network (CNN) algorithms used in different stages and a binocular eye-in-hand system on the end effector, which detects the position and orientation of workpiece. Both algorithms are trained by

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17

Allen, P., P. Michelman, and K. Roberts. "Current research in robotics and automation-an intelligent grasping system." Computer 22, no. 3 (1989): 50–52. http://dx.doi.org/10.1109/2.16225.

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18

Jiabu, Han, Chen Saixuan, Cui Guohua, Cheng Zhenyi, and Li Shipei. "Research on Intelligent Grasping System of Monocular Vision Guided Manipulator." IOP Conference Series: Materials Science and Engineering 616 (October 16, 2019): 012025. http://dx.doi.org/10.1088/1757-899x/616/1/012025.

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19

Xu, Linfeng, Gang Li, Peiheng Song, and Weixiang Shao. "Vision-Based Intelligent Perceiving and Planning System of a 7-DoF Collaborative Robot." Computational Intelligence and Neuroscience 2021 (September 14, 2021): 1–25. http://dx.doi.org/10.1155/2021/5810371.

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In this paper, an intelligent perceiving and planning system based on deep learning is proposed for a collaborative robot consisting of a 7-DoF (7-degree-of-freedom) manipulator, a three-finger robot hand, and a vision system, known as IPPS (intelligent perceiving and planning system). The lack of intelligence has been limiting the application of collaborative robots for a long time. A system to realize “eye-brain-hand” process is crucial for the true intelligence of robots. In this research, a more stable and accurate perceiving process was proposed. A well-designed camera system as the visio

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20

Kumičáková, Darina, and Martin Jakubčík. "Specialised Robotic Hand Designing and Object Grasping Simulation." Applied Mechanics and Materials 282 (January 2013): 90–98. http://dx.doi.org/10.4028/www.scientific.net/amm.282.90.

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New Trend in the Specialised Robotic Hand Designing is Accelerated by Requirements on both Flexible and Intelligent Automated Manipulation with Objects of Different Types. Increasingly more Sophisticated Simulation Tools for Robotic Hand 3D Model Testing are Developed by Research Teams on the Whole World. the Article Presents the Methodology of the Robotic Hand Simulation Model Creation with the Goal to Test its Ability to Grasp the Object.

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21

Huang, Shiuh Jer, Wei Han Chang, and Jui Yiao Su. "Intelligent Robotic Gripper Control Strategy." Advanced Materials Research 753-755 (August 2013): 2006–9. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2006.

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Although, on-off control robot gripper is widely employed in pick-and-place operations, it can not be applied in fragile or soft objects handling. Here, an intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of grasped object mass and soft/hard features. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown b

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22

MORALES, ANTONIO, ERIS CHINELLATO, ANDREW H. FAGG, and ANGEL P. DEL POBIL. "USING EXPERIENCE FOR ASSESSING GRASP RELIABILITY." International Journal of Humanoid Robotics 01, no. 04 (2004): 671–91. http://dx.doi.org/10.1142/s0219843604000290.

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Autonomous manipulation is a key issue for a humanoid robot. Here, we are interested in a vision-based grasping behavior so that the robot can deal with previously unknown objects in real time and in an intelligent manner. Starting from a number of feasible candidate grasps, we focus on the problem of predicting their reliability using the knowledge acquired in previous grasping experiences. A set of visual features which take into account physical properties that can affect the stability and reliability of a grasp are defined. A humanoid robot obtains its grasping experience by repeating a la

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23

Ji, Shan-Qian, Ming-Bao Huang, and Han-Pang Huang. "Robot Intelligent Grasp of Unknown Objects Based on Multi-Sensor Information." Sensors 19, no. 7 (2019): 1595. http://dx.doi.org/10.3390/s19071595.

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Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without knowing their mass and damping properties. Therefore, this study combined vision and robot hand real-time grasp control action to achieve reliable and accurate object grasping in a cluttered scene. An efficient online algorithm for collision-free grasping pose generation according to a bounding box is proposed, and the g

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24

Sun, Bing, Wen Pang, Mingzhi Chen, and Daqi Zhu. "Development and experimental verification of search and rescue ROV." Intelligence & Robotics 2, no. 4 (2022): 355–70. http://dx.doi.org/10.20517/ir.2022.23.

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This paper presents the design of a new type of search and rescue remotely operated vehicle (ROV) system. The goal is to achieve the underwater target search and detection and small target capture and rescue operation requirements. First, the overall design of the whole underwater surface system and the layout design of the propulsion system are given. On this basis, the ROV frame structure, electronic cabin, and power cabin are designed and analyzed. To accomplish the grasping task, a grasping hand is designed based on a multifunctional manipulator to achieve underwater grasping. To make the

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25

Wang, Xiao Feng. "The Application of the Internet of Things in Intelligent Digital Campus." Applied Mechanics and Materials 543-547 (March 2014): 3503–6. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3503.

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From the starting of understanding the concept and characteristics of the Internet of Things, I propose the concept of the intelligent digital campus according to the research status grasping of the Internet of Things. The intelligent digital campus based on the Internet of Things can achieve automatic recognition, library borrowing management, multimedia classroom and laboratory management and campus consumption management. It provides more efficient services for teachers and students work, studying and daily life.

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26

Song, Yuchen, and zhijiang xie. "Control method of three-wheel intelligent tracking logistics car based on OpenMV4." Journal of Physics: Conference Series 2246, no. 1 (2022): 012033. http://dx.doi.org/10.1088/1742-6596/2246/1/012033.

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Abstract The author designs an intelligent lifting transport device based on STM32 microcontroller. The lifting device is designed with STM32F407ZGT6 as the control core, and the hardware consists of OpenMV4 recognition, gray tracking, motor drive and other modules. It can carry out automatic, gray tracking, OpenMV4 recognition, intelligent material grasping and other functions, and can follow the track black line on the set plane track, and complete the preset operation. The experimental results are good.

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27

Bao, Xiujuan, Lianhui Li, Weiqiang Ou, and Lu Zhou. "Robot intelligent grasping experimental platform combining Jetson NANO and machine vision." Journal of Physics: Conference Series 2303, no. 1 (2022): 012053. http://dx.doi.org/10.1088/1742-6596/2303/1/012053.

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Abstract According to the requirements of the intelligent manufacturing experimental teaching project, this experimental platform develops a robot intelligent grasping experimental platform based on Jetson NANO and vision. The platform adopts the Jetson NANO controller based on Google open source machine learning framework TensorFlow + Keras, cooperates with the computer vision library OpenCV to develop the machine vision algorithm, and realizes the grasping and handling of materials based on a 6-DOF articulated robot, it can support open project-based experimental teaching. The experimental p

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Lee, Brielle J. B., Adam Williams, and Pinhas Ben-Tzvi. "Intelligent Object Grasping With Sensor Fusion for Rehabilitation and Assistive Applications." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 8 (2018): 1556–65. http://dx.doi.org/10.1109/tnsre.2018.2848549.

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29

Yang, Yang, and Qi-Xin Cao. "Monocular vision based 6D object localization for service robot’s intelligent grasping." Computers & Mathematics with Applications 64, no. 5 (2012): 1235–41. http://dx.doi.org/10.1016/j.camwa.2012.03.067.

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30

Bezák, Pavol. "Improving Cognitive Skills of the Industrial Robot." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 23, s1 (2015): 19–28. http://dx.doi.org/10.1515/rput-2015-0023.

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Abstract At present, there are plenty of industrial robots that are programmed to do the same repetitive task all the time. Industrial robots doing such kind of job are not able to understand whether the action is correct, effective or good. Object detection, manipulation and grasping is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. In this paper, the proposal of an intelligent humanoid hand object detection and grasping model is presented assuming that the object properties are known. The control is simulated in the Matlab

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31

Liu, Huwei, Li Zhou, Junhui Zhao, et al. "Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations." Sustainability 14, no. 13 (2022): 7781. http://dx.doi.org/10.3390/su14137781.

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In order to explore the application of robots in intelligent supply-chain and digital logistics, and to achieve efficient operation, energy conservation, and emission reduction in the field of warehousing and sorting, we conducted research in the field of unmanned sorting and automated warehousing. Under the guidance of the theory of sustainable development, the ESG (Environmental Social Governance) goals in the social aspect are realized through digital technology in the storage field. In the picking process of warehousing, efficient and accurate cargo identification is the premise to ensure

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Mohammed, Marwan Qaid, Lee Chung Kwek, Shing Chyi Chua, et al. "Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion." Sustainability 13, no. 24 (2021): 13686. http://dx.doi.org/10.3390/su132413686.

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In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a rand

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Nambiappan, Harish Ram, Stephanie Arevalo Arboleda, Cody Lee Lundberg, Maria Kyrarini, Fillia Makedon, and Nicholas Gans. "MINA: A Robotic Assistant for Hospital Fetching Tasks." Technologies 10, no. 2 (2022): 41. http://dx.doi.org/10.3390/technologies10020041.

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In this paper, a robotic Multitasking Intelligent Nurse Aid (MINA) is proposed to assist nurses with everyday object fetching tasks. MINA consists of a manipulator arm on an omni-directional mobile base. Before the operation, an augmented reality interface was used to place waypoints. Waypoints can indicate the location of a patient, supply shelf, and other locations of interest. When commanded to retrieve an object, MINA uses simultaneous localization and mapping to map its environment and navigate to the supply shelf waypoint. At the shelf, MINA builds a 3D point cloud representation of the

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34

Deng, Songbo, He Cai, Ke Li, Tongtong Li, Tao Deng, and Yanbo Wang. "The Design of Intelligent Grasping Control System for a Special Operation Manipulator." IOP Conference Series: Materials Science and Engineering 428 (October 1, 2018): 012005. http://dx.doi.org/10.1088/1757-899x/428/1/012005.

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35

Burkhard, Natalie T., Mark R. Cutkosky, and J. Ryan Steger. "Slip Sensing for Intelligent, Improved Grasping and Retraction in Robot-Assisted Surgery." IEEE Robotics and Automation Letters 3, no. 4 (2018): 4148–55. http://dx.doi.org/10.1109/lra.2018.2863360.

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36

Ikeda, Katsuyuki, Peng Chen, Mitushi Yamashita, and Takenori Teraoka. "256 Study on intelligent grasping control of robot hand for agricultural production." Proceedings of Conference of Tokai Branch 2009.58 (2009): 119–20. http://dx.doi.org/10.1299/jsmetokai.2009.58.119.

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37

Wang, Chao, Xuehe Zhang, Xizhe Zang, et al. "Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review." Sensors 20, no. 13 (2020): 3707. http://dx.doi.org/10.3390/s20133707.

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and ph

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Li, Yaowei, Fei Guo, Miaotian Zhang, et al. "A Novel Deep Learning-Based Pose Estimation Method for Robotic Grasping of Axisymmetric Bodies in Industrial Stacked Scenarios." Machines 10, no. 12 (2022): 1141. http://dx.doi.org/10.3390/machines10121141.

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A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neur

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Wang, Hui, Jieren Cheng, Yichen Xu, Sirui Ni, Zaijia Yang, and Jiangpeng Li. "A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms." Security and Communication Networks 2023 (June 1, 2023): 1–16. http://dx.doi.org/10.1155/2023/7723164.

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Robot grasping is one of the most important abilities of modern intelligent robots, especially industrial robots. However, most of the existing robot arm’s grasp detection work is highly dependent on their edge computing ability, and the safety problems in the process of grasp detection are not considered enough. In this paper, we propose a new robotic arm grasping detection model with an edge-cloud collaboration method. With the scheme of multi-object multi-grasp, our model improves the mission success ratio of grasping. The model can not only complete the compression of full-resolution image

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40

Cheng, Li-Wei, Shih-Wei Liu, and Jen-Yuan Chang. "Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping." Applied Sciences 12, no. 10 (2022): 5024. http://dx.doi.org/10.3390/app12105024.

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With the advancement of robotic technologies, more and more tasks in industrial and commercial applications rely on the use of robots to assist or even replace humans. To fulfill the needs of grasping and handling different objects, the development of a universal grasping device acting as an end-effector to a robotic manipulator has been one of the main robotic research and development focuses. Therefore, this study was aimed at the development of a general robotic gripper with three fingers for adaptive actuation and an eye-in-hand vision system. With the adaptive actuation feature, each fing

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41

Plermkamon, Supattra, and Nitin Afzulpurkar. "An intelligent real-time tracking and grasping system for a robotic work cell." Advanced Robotics 17, no. 5 (2003): 403–25. http://dx.doi.org/10.1163/15685530360663418.

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42

Sayour, Malak H., Sharbel E. Kozhaya, and Samer S. Saab. "Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects." Journal of Robotics 2022 (June 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/2585656.

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Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquir

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Tang, Bo. "Neural Network Based on Work Piece Recognition and Robot Intelligent Capture in Complex Environments." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 07 (2019): 2059022. http://dx.doi.org/10.1142/s0218001420590223.

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In today’s rapid development of science and technology, the manual work in the factory has been gradually replaced by machines, and the process of industrial intelligence has been further deepened. Workpiece recognition is the use of machine learning, computer vision and other technologies to identify the target workpiece, and the robot intelligent capture is a higher level of operation of the workpiece recognition, which is the key to realize the intelligentization of industrial robots. Due to the complex environmental factors and the diversity of the shape and size of the objects to be grasp

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Liu, Tian-Hu, Yong-Lu Wen, Gui-Qi Li, and Xiang-Ning Nie. "Optimization and Experimental Study of an Intelligent Bamboo-Splitting Machine Charging Manipulator." Journal of Robotics 2020 (March 17, 2020): 1–10. http://dx.doi.org/10.1155/2020/4675301.

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A nonautomatic bamboo-splitting machine must charge with material and change tools manually. However, manual charging is very dangerous. An intelligent bamboo-splitting machine can feed automatically and change tools intelligently and has broad application prospects. A charging manipulator is an important part of an intelligent bamboo-splitting machine. The size of the manipulator was optimized here using a genetic algorithm. The capture rate, centering rate, and dynamic characteristics of an intelligent bamboo-splitting machine charging manipulator, in which key factors were considered, were

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Liu, Shijie, Zhaoyou Ma, Xinming Guo, et al. "Research and development of intelligent safety sensor integration devices for autonomous driving." Journal of Physics: Conference Series 2196, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1742-6596/2196/1/012002.

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Abstract Grasping the status of vehicles timely and accurately is the key to avoid traffic accidents and collecting data from multi-sensors is significant for development of road-side intelligent sensor technology. In this paper, LiDAR (Light Detection and Ranging) and camera sensors are tested, and the sensor data can be processed and analyzed, and the display can be integrated. In this paper, a multi-sensor data acquisition integration device based on Raspberry Pi is proposed, which can realize the optimal processing of data and simultaneous acquisition display function. This method avoids t

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Dolga, Valer, and Lia Dolga. "The Design Approach of Human Prostheses and Intelligent Robot End Effectors Based on Mechatronic Philosophy." Solid State Phenomena 166-167 (September 2010): 167–72. http://dx.doi.org/10.4028/www.scientific.net/ssp.166-167.167.

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The grip operation requires grippers as end effectors, behaving analogous to the human grasping organ, irrespectively if it is performed in robotic systems or in arm prostheses. Improving grippers design involves a biomechanical analysis of the human prehensile organ. The authors evaluated the complexity of both industrial and medical grippers and proposed a common design method, based on the mechatronic philosophy. The aimed upshot might be a better device, with improved functionality, a cost cutback and a more efficient design endeavour.

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Liu, Tao, Yin Guo, Shourui Yang, Shibin Yin, and Jigui Zhu. "Monocular-Based 6-Degree of Freedom Pose Estimation Technology for Robotic Intelligent Grasping Systems." Sensors 17, no. 2 (2017): 334. http://dx.doi.org/10.3390/s17020334.

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Jia, Jiaqi, Min Fu, Xuefeng Liu, and Bing Zheng. "Underwater Object Detection Based on Improved EfficientDet." Remote Sensing 14, no. 18 (2022): 4487. http://dx.doi.org/10.3390/rs14184487.

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Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channel

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Zhang, Jianjun, Weidong Liu, Li’e Gao, Yiwen Zhang, and Weijiang Tang. "Design, Analysis and Experiment of a Tactile Force Sensor for Underwater Dexterous Hand Intelligent Grasping." Sensors 18, no. 8 (2018): 2427. http://dx.doi.org/10.3390/s18082427.

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This paper proposes a novel underwater dexterous hand structure whose fingertip is equipped with underwater tactile force sensor (UTFS) array to realize the grasping sample location determination and force perception. The measurement structure, theoretical analysis, prototype development and experimental verification of the UTFS are purposefully studied in order to achieve accurate measurement under huge water pressure influence. The UTFS is designed as capsule shape type with differential pressure structure, and the external water pressure signal is separately transmitted to the silicon cup b

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Wang, Yongzhi. "Effective Integration of Computer and Ideological and Political Education Based on Intelligent Teaching Platform." MATEC Web of Conferences 365 (2022): 01018. http://dx.doi.org/10.1051/matecconf/202236501018.

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The traditional mechanism of ideological and political education is relatively single, no matter it is teaching idea, or teaching method, there are great limitations, which affect the effectiveness of ideological and political education. In the environment of wisdom education, we will give full play to the advantages of computer technology and reconstruct the system of ideological and political education. We will innovate the effectiveness of ideological and political education on the basis of accurately grasping students' demands for ideological and political study, improving students' unders

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