State of the Art Robotic Grippers and Applications

Tai, Kevin; El-Sayed, Abdulrahman M.; Shahriari, Mohammadali; Biglarbegian, Mohammad; Mahmud, Shohel

doi:10.3390/robotics5020011

Cited by 167 publications

(63 citation statements)

References 114 publications

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“…Furthermore, the deformable material is usually soft and elastic, which is intractable to perform a cutting procedure accurately [9]. Therefore, it is necessary to use a gripper [10], [11], which holds a point (pinch point) on the sheet and tensions it along an allowable set of directions with a reasonable force while a surgical scissor is used to cut through a contour trajectory, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

Nguyen

Nahavandi

et al. 2019

2019 IEEE International Systems Conference (SysCon)

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In robotic surgery, pattern cutting through a deformable material is a challenging research field. The cutting procedure requires a robot to concurrently manipulate a scissor and a gripper to cut through a predefined contour trajectory on the deformable sheet. The gripper ensures the cutting accuracy by nailing a point on the sheet and continuously tensioning the pinch point to different directions while the scissor is in action. The goal is to find a pinch point and a corresponding tensioning policy to minimize damage to the material and increase cutting accuracy measured by the symmetric difference between the predefined contour and the cut contour. Previous study considers finding one fixed pinch point during the course of cutting, which is inaccurate and unsafe when the contour trajectory is complex. In this paper, we examine the soft tissue cutting task by using multiple pinch points, which imitates human operations while cutting. This approach, however, does not require the use of a multi-gripper robot. We use a deep reinforcement learning algorithm to find an optimal tensioning policy of a pinch point. Simulation results show that the multi-point approach outperforms the state-ofthe-art method in soft pattern cutting task with respect to both accuracy and reliability.

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Section: Introductionmentioning

confidence: 99%

Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

Nguyen

Nahavandi

et al. 2019

2019 IEEE International Systems Conference (SysCon)

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“…Conventional soft vacuum suction grippers in the form of cups or bellows, on the other hand, overcome some of these challenges having simple, robust and low cost designs, but lack universality. There are a variety of fixed suction cup geometries from a few millimeters to a few decimeters circular-opening diameter, oval aperture for picking long objects, and with different compliance for lifting solid metal, fragile glass, or flexible textile fabric [11]. Hence, gripping objects of different shape and stiffness with a single cup is impossible without replacement, which is demanding and time consuming.…”

Section: Introductionmentioning

confidence: 99%

An Origami-Inspired Reconfigurable Suction Gripper for Picking Objects With Variable Shape and Size

Zhakypov

Heremans

Billard

et al. 2018

IEEE Robot. Autom. Lett.

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Gripper adaptability to handle objects of different shape and size brings high flexibility to manipulation. Gripping flat, round, or narrow objects poses challenges to even the most sophisticated robotic grippers. Among various gripper technologies, the vacuum suction grippers provide design simplicity, yet versatility at low cost, however, their application is limited to their fixed shape and size. Here, we present an origamiinspired reconfigurable suction gripper to address adaptability with robotic suction grippers. Constructed from rigid and soft components and driven by compact shape memory alloy actuators, the gripper can effectively self-fold into three shape modes to pick large and small flat, narrow cylindrical, triangular and spherical objects. The 10-g few centimeters gripper, lifts loads up to 5 N, 50 times its weight. We also present an underactuated prototype, demonstrating the versatility of our design and actuation methods.

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“…Hundreds of different micro-systems have indeed been reported in the literature [1,2], with different operational strategies [3] and being mainly dedicated to medical or biological applications. Among others, an untethered mobile microgripper controlled by an external electromagnetic coil system has been reported [4], where the manipulation of different microgels was successfully demonstrated with a pick-and-place robotic heterogeneous 3D assembly technique.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, there are few examples about a real use of microsystems for surgical purposes or for in situ biological tissues grasping [2]. Notably, none of these examples consider the possible side-effects such as protein fouling on microscale tools, which could adversely affect the outcome of surgical operations or biopsies.…”

Section: Introductionmentioning

confidence: 99%

Innovative Silicon Microgrippers for Biomedical Applications: Design, Mechanical Simulation and Evaluation of Protein Fouling

et al. 2018

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Abstract:The demand of miniaturized, accurate and robust micro-tools for minimally invasive surgery or in general for micro-manipulation, has grown tremendously in recent years. To meet this need, a new-concept comb-driven microgripper was designed and fabricated. Two microgripper prototypes differing for both the number of links and the number of conjugate surface flexure hinges are presented. Their design takes advantage of an innovative concept based on the pseudo-rigid body model, while the study of microgripper mechanical potentialities in different configurations is supported by finite elements' simulations. These microgrippers, realized by the deep reactive-ion etching technology, are intended as micro-tools for tissue or cell manipulation and for minimally invasive surgery; therefore, their biocompatibility in terms of protein fouling was assessed. Serum albumin dissolved in phosphate buffer was selected to mimic the physiological environment and its adsorption on microgrippers was measured. The presented microgrippers demonstrated having great potential as biomedical tools, showing a modest propensity to adsorb proteins, independently from the protein concentration and time of incubation.

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State of the Art Robotic Grippers and Applications

Cited by 167 publications

References 114 publications

Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery

An Origami-Inspired Reconfigurable Suction Gripper for Picking Objects With Variable Shape and Size

Innovative Silicon Microgrippers for Biomedical Applications: Design, Mechanical Simulation and Evaluation of Protein Fouling

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