Design and Implementation of a Multi-Function Gripper for Grasping General Objects

Kang, Long; Seo, Jong-Tae; Kim, Sanghwa; Wanju, Kim

doi:10.3390/app9245266

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…Apart from these two categories into which the four grippers described in this research fall, many other technologies have been used: electro-adhesion [ 29 , 30 , 31 ], Gecko-adhesion [ 32 , 33 ], dielectric elastomer actuators (DEAs) [ 34 , 35 , 36 , 37 ], fluidic elastomer actuators (FEAs) [ 38 , 39 ], magnetorheological (MR) fluid [ 40 ], and shape-memory materials [ 41 ]. In some cases, two different types of actuation are implemented in the same gripper to allow stable and reliable grasping under different working environments as in [ 42 ], which presents the combination of the suction and traditional linkage-driven grippers or in [ 18 ], which presents the integration of an electro-adhesive system and a pneumatic actuator.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Different Technologies for Soft Robotics Grippers

Terrile

Argüelles

Barrientos

2021

Sensors

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Soft grippers have experienced a growing interest due to their considerable flexibility that allows them to grasp a variety of objects, in contrast to hard grippers, which are designed for a specific item. One of their most remarkable characteristics is the ability to manipulate soft objects without damaging them. This, together with their wide range of applications and the use of novels materials and technologies, renders them a very robust device. In this paper, we present a comparison of different technologies for soft robotics grippers. We fabricated and tested four grippers. Two use pneumatic actuation (the gripper with chambered fingers and the jamming gripper), while the other two employ electromechanical actuation (the tendon driver gripper and the gripper with passive structure). For the experiments, a group of twelve objects with different mechanical and geometrical properties have been selected. Furthermore, we analyzed the effect of the environmental conditions on the grippers, by testing each object in three different environments: normal, humid, and dusty. The aim of this comparative study is to show the different performances of different grippers tested under the same conditions. Our findings indicate that we can highlight that the mechanical gripper with a passive structure shows greater robustness.

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Section: Related Workmentioning

confidence: 99%

Comparison of Different Technologies for Soft Robotics Grippers

Terrile

Argüelles

Barrientos

2021

Sensors

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“…Nowadays, hybrid approaches, which combines fully actuated fingers for precise manipulation and underactuated ones for power grasp and compliance, are becoming popular [34,35]. A different hybrid approach is presented in [36], where authors proposed an underactuated gripper with a suction cup for picking up various objects in different working environments. Such hybrid solution allows for building multifunction robotic cells [37] for maximising the production rate.…”

Section: Underactuated and Soft Grippersmentioning

confidence: 99%

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

et al. 2021

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Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation.

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“…Overall, the gripper length is 411 mm from the base bottom to the fully extended suction cup. So far, several grippers combining a two-fingered gripper and a suction gripper have been developed [30,31]. Since the fingertip trajectory of these grippers are non-linear, strategies for inserting a finger in between arranged objects are difficult to adopt.…”

Section: Developed Grippermentioning

confidence: 99%

Modeling object arrangement patterns and picking arranged objects

Nagata

Nishi

2021

Advanced Robotics

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This study investigates object picking focusing on object arrangement patterns. Objects stored in distribution warehouses or stores are arranged in regular patterns, and the grasping strategy for object picking is selected according to the object arrangement pattern. However, object arrangement patterns have not been modeled for object picking. In this study, we represent objects as polyhedral primitives, such as cuboids or hexagonal cylinders, and model object arrangements by considering occlusion patterns for object model surfaces and considering whether the adjacent object occluding the surface is moveable. We define grasp patterns based on combinations of the grasp surfaces and discuss the grasping strategy when the grasp surfaces are occluded by adjacent objects. We then introduce newly developed gripper for picking arranged objects. The gripper comprises a suction gripper and a two-fingered gripper. The suction gripper has a telescopic arm and a swing suction cup. The two-fingered gripper mechanism combines a Scott Russell linkage and a parallel link. This mechanism is advantageous for the gripper in reaching narrow spaces and inserting fingers between objects. We demonstrate the picking up of arranged objects using the grippers.

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Design and Implementation of a Multi-Function Gripper for Grasping General Objects

Cited by 22 publications

References 26 publications

Comparison of Different Technologies for Soft Robotics Grippers

Comparison of Different Technologies for Soft Robotics Grippers

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Modeling object arrangement patterns and picking arranged objects

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