Relevant bibliographies by topics / Robot Gripper

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  1. Journal articles

Academic literature on the topic 'Robot Gripper'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Robot Gripper"

1

Yang, Yang, Kaixiang Jin, Honghui Zhu, Gongfei Song, Haojian Lu, and Long Kang. "A 3D-Printed Fin Ray Effect Inspired Soft Robotic Gripper with Force Feedback." Micromachines 12, no. 10 (2021): 1141. http://dx.doi.org/10.3390/mi12101141.

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Soft robotic grippers are able to carry out many tasks that traditional rigid-bodied grippers cannot perform but often have many limitations in terms of control and feedback. In this study, a Fin Ray effect inspired soft robotic gripper is proposed with its whole body directly 3D printed using soft material without the need of assembly. As a result, the soft gripper has a light weight, simple structure, is enabled with high compliance and conformability, and is able to grasp objects with arbitrary geometry. A force sensor is embedded in the inner side of the gripper, which allows the contact force required to grip the object to be measured in order to guarantee successful grasping and to provide the most suitable gripping force. In addition, it enables control and data monitoring of the gripper’s operating state at all times. Characterization and grasping demonstration of the gripper are given in the Experiment section. Results show that the gripper can be used in a wide range of scenarios and applications, such as the service robot and food industry.
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2

Jamaludin, A. S., M. N. M. Razali, N. Jasman, A. N. A. Ghafar, and M. A. Hadi. "Design of spline surface vacuum gripper for pick and place robotic arms." Journal of Modern Manufacturing Systems and Technology 4, no. 2 (2020): 48–55. http://dx.doi.org/10.15282/jmmst.v4i2.5181.

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The gripper is the most important part in an industrial robot. It is related with the environment around the robot. Today, the industrial robot grippers have to be tuned and custom made for each application by engineers, by searching to get the desired repeatability and behaviour. Vacuum suction is one of the grippers in Watch Case Press Production (WCPP) and a mechanism to improve the efficiency of the manufacturing procedure. Pick and place are the important process for the annealing process. Thus, by implementing vacuum suction gripper, the process of pick and place can be improved. The purpose of vacuum gripper other than design vacuum suction mechanism is to compare the effectiveness of vacuum suction gripper with the conventional pick and place gripper. Vacuum suction gripper is a mechanism to transport part and which later sequencing, eliminating and reducing the activities required to complete the process. Throughout this study, the process pick and place became more effective, the impact on the production of annealing process is faster. The vacuum suction gripper can pick all part at the production which will lower the loss of the productivity. In conclusion, vacuum suction gripper reduces the cycle time about 20%. Vacuum suction gripper can help lower the cycle time of a machine and allow more frequent process in order to increase the production flexibility.
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3

Qiaoling, Du, Lu Xinpo, Wang Yankai, and Liu Sinan. "The obstacle-surmounting analysis of a pole-climbing robot." International Journal of Advanced Robotic Systems 17, no. 6 (2020): 172988142097914. http://dx.doi.org/10.1177/1729881420979146.

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Surmounting obstacles during continuously climbing in a complex environment is an important issue for pole-climbing robots. An obstacle-surmounting strategy is presented for a pole-climbing robot. The force and moment applied on the pole-climbing robot in static status were analyzed, and the analysis of pole-climbing robot’s upward vertical climbing was conducted. The climbing execution has four steps: loosening the lower gripper, curling up, striding forward, and clamping the upper gripper. To obtain the information of obstacle crossing accurately, the obstacle-surmounting conditions were analyzed in detail. We modeled the striding linkage with thickness and obtained the Denavit–Hartenberg coordinates of each vertex. The model of the grippers with thickness was proposed and the Denavit–Hartenberg coordinates of each vertex of the grippers were obtained. Then single-step negotiating an obstacle and multistep negotiating an obstacle were proposed. Experiments were conducted to verify the effectiveness of the obstacle-surmounting strategy.
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4

Cho, Jeoung S., Eric M. Malstromt, and John C. Even. "Use of coding and classification systems in the design of universal robotic grippers." Robotica 11, no. 4 (1993): 345–50. http://dx.doi.org/10.1017/s026357470001660x.

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SUMMARYRecent hardware advances for robot accessories include self changing grippers. A universal wrist has the capability of accessing and pneumatically attaching itself to a limited number of grippers that can be stored in a magazine. This paper addresses the determination of self changing gripper characteristics to permit the grasping of a wide variety of geometric shapes with a limited number of different gripper types.
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5

Jaison, Jerry, Vasudevan Nagaraj, and Arockia Selvakumar A. "A Novel Gripper design for Diaphragm spring plate Pick and Place Cobot." International Journal of Engineering & Technology 7, no. 4.36 (2018): 394. http://dx.doi.org/10.14419/ijet.v7i4.36.23812.

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Robot integrated manufacturing has turned out to be the future of manufacturing automation technology. Worker assisting robots perform simultaneously operation including machining, assembly, inspection, material handling etc. and in some case multiple operations in the same system at a faster and precise rate. Collaborative robots are human and computer controlled hybrid material handling device which facilitates the concept of shared workspace. To exploit its function, gripper mechanisms are inevitable. This project focuses on the design of cobotic grippers for pick and place application. Of the different concepts compared a suitable design is chosen. The gripper moves according to the signals received from the cobot, sensors and PLC control system.
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6

Velineni, Poornesh, Jayasuriya Suresh, Naveen Kumar C, and Suresh M. "Design of Pneumatic Gripper for Pick and Place Operation (Four Jaw)." International Research Journal of Multidisciplinary Technovation 2, no. 2 (2020): 1–8. http://dx.doi.org/10.34256/irjmt2021.

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Grippers are attached at the end of an industrial arm robot for material handling purpose. Grippers plays a major role in all pick and place application industries. Those are connected as end effectors to realize and develop a task in an industrial work floor. Pneumatic gripper works with the principle of compressed air. The gripper is connected to a compressed air supply. When air pressure is applied on the piston, the gripper gets opened while the air gets exist from the piston it gets closed. It is possible to control the force acting on the gripper by controlling the air pressure with the help of the valve. The objective is to design an effective, simple, and economic gripper for pick and place application.
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7

Zhang, Mike Tao, and Ken Goldberg. "Designing robot grippers: optimal edge contacts for part alignment." Robotica 25, no. 3 (2006): 341–49. http://dx.doi.org/10.1017/s0263574706003134.

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SUMMARYAlthough parallel-jaw grippers play a vital role in automated manufacturing, gripper surfaces are still designed by trial-and-error. This paper presents an algorithmic approach to designing gripper jaws that mechanically align parts in the vertical (gravitational) plane. We consider optimal edge contacts, based on modular trapezoidal segments that maximize contact between the gripper and the part at its desired final orientation. Given then-sided 2D projection of an extruded convex polygonal part, mechanical properties such as friction and center of mass, and initial and desired final orientations, we present anO(n3logn) numerical algorithm to design optimal gripper jaws. We also present anO(nlogn) algorithm to compute tolerance classes for these jaws, and report on an online implemented version of the algorithm and physical experiments with the jaws it designed. This paper extends earlier results that generated optimal point contacts [M. T. Zhang and K. Goldberg, “Gripper point contacts for part alignment,”IEEE Trans. Robot. Autom.18(6), 902–910 (2002)].
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8

Shin, Dong Hwan, Choong Pyo Jeong, Tae Sang Park, Yoon Gu Kim, and Ji Nung An. "Algorithm for the Extraction of Optimal Gripping Force Range with the Robot Gripper." Applied Mechanics and Materials 251 (December 2012): 164–68. http://dx.doi.org/10.4028/www.scientific.net/amm.251.164.

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The arm of robot consists of a manipulator and an end-effector. The end-effector is doing a specific work such as welding, picking and placing, sawing, deburring, suction, etc. with for the specific object of robot system. Here we are focused on the gripper among end-effectors. If the gripper generates the excessive gripping force, then the gripped material can have a permanent deformation. On the other hand, if the gripping force is too small, then the gripped material can be slip from the end-effector, drop to the floor and will get damaged. Therefore, it is important to use the adequate gripping force of the gripper. In this paper, we suggest the algorithm which is easy to automate, for the extraction of optimal gripping force range, with the estimation of frictional coefficient and young’s modulus.
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9

Romeo, Rocco Antonio, Michele Gesino, Marco Maggiali, and Luca Fiorio. "Combining Sensors Information to Enhance Pneumatic Grippers Performance." Sensors 21, no. 15 (2021): 5020. http://dx.doi.org/10.3390/s21155020.

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The gripper is the far end of a robotic arm. It is responsible for the contacts between the robot itself and all the items present in a work space, or even in a social space. Therefore, to provide grippers with intelligent behaviors is fundamental, especially when the robot has to interact with human beings. As shown in this article, we built an instrumented pneumatic gripper prototype that relies on different sensors’ information. Thanks to such information, the gripper prototype was able to detect the position of a given object in order to grasp it, to safely keep it between its fingers and to avoid slipping in the case of any object movement, even very small. The gripper performance was evaluated by means of a generic grasping algorithm for robotic grippers, implemented in the form of a state machine. Several slip tests were carried out on the pneumatic gripper, which showed a very fast response time and high reliability. Objects of various size, shape and hardness were employed to reproduce different grasping scenarios. We demonstrate that, through the use of force, torque, center of pressure and proximity information, the behavior of the developed pneumatic gripper prototype outperforms the one of the traditional pneumatic gripping devices.
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10

Mahler, Jeffrey, Matthew Matl, Vishal Satish, et al. "Learning ambidextrous robot grasping policies." Science Robotics 4, no. 26 (2019): eaau4984. http://dx.doi.org/10.1126/scirobotics.aau4984.

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Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. This paper explores “ambidextrous” robot grasping, where two or more heterogeneous grippers are used. We present Dexterity Network (Dex-Net) 4.0, a substantial extension to previous versions of Dex-Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry. We train policies for a parallel-jaw and a vacuum-based suction cup gripper on 5 million synthetic depth images, grasps, and rewards generated from heaps of three-dimensional objects. On a physical robot with two grippers, the Dex-Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour.
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