A statistical review of industrial robotic grippers

Birglen, Lionel; Schlicht, Thomas

doi:10.1016/j.rcim.2017.05.007

Cited by 126 publications

(42 citation statements)

References 12 publications

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“…Besides, a low standard deviation is indicated in both cases that is 0.86 and 1.14. In Table. 5 the event based method performance and the number of force adjustments is recorded for Objects (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11). The average slip measure from the overall objects handled is 1.4 mm and gives a low standard deviation 0.77 is observed.…”

Section: Slip Detection and Suppression Experiments And Resultsmentioning

confidence: 99%

“…With the emergence of Industry 4.0 [1] and increased use of service robots in several application domains [2], there is a growing demand for advanced perception capabilities in robotic systems to especially tackle the uncertainties occurring during physical tasks. Most of today's robots are equipped with parallel grippers or sophisticated hands which allows them to perform simple grasping to dexterous manipulation tasks [3] in both structured and unstructured environment. Slip incidents are common while performing such tasks under those settings.…”

Section: Introductionmentioning

confidence: 99%

“…A class of objects handled by the Baxter robot using event-based finger vision system: In column 1, objects(1)(2)(3)(4)(5)(6)(7)(8)(9)(10) caged by the robotic gripper is presented in a row order; Column 2 illustrates the associated feature-based slip detection and intelligent control action to suppress slip while lifting, manipulating and placing; Column 3 shows the initial slip instance while lifting the caged object in an event frame. The online method enables the slip detector and fuzzy controller to adapt and handle objects of different shape and size.…”

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confidence: 99%

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Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

et al. 2020

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Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise and object feature events in real-time to improve slip detection and suppression. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. For a class of objects, results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz (∆t = 500µs) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.

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Section: Slip Detection and Suppression Experiments And Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

et al. 2020

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“…To fix such deficiency, the application of pneumatic manipulator has been widely introduced [ 18 ], grasping the objects by activating a powerful inward suction force within the suction cups, which enables the movement of the object [ 19 ]. Nevertheless, this full open-close tool is not suitable for the installation of sensors, as while the whole process can be simply described as the charge and the release of the gases, the intermediate process is difficult to control with a lack of gripping dexterity as well [ 20 ]. More research is needed for the manipulator with good flexibility and excellent adaptability [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Multi-Sensor Network for Adaptive Grasping of Fragile Fruits: Design and Feasibility Tests

Xie

Zhang

Zhou

et al. 2020

Sensors

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Secure grasping of fragile fruits and other agricultural products without potential slip and damage is still a challenge due to the size and shape varying, bruise susceptible, as well as hardness changing during fruit and vegetable maturation. In the robotic grasping process, the mechanical damage mainly depends upon the aggressiveness of the gripper and the sensitivity of the product to the damage. In this study, a flexible gripper integrated with multi-sensor network is designed and tested. The network proposed includes three kinds of sensors that enable the gripper to grasp various products with the sense of touch and visual perception. Particular attention has been attached to the sensors applied between the fingers, and this makes sensing and grasping capabilities improved. To create an accurate grasping system, a grasping algorithm and the force control model are proposed for any bending state based on Cosserat theory. The boundary detection is included in the grasping algorithm, detecting the shape edge by some certain point calculation. The created grasping system guarantees mechanical compliance by evaluating and adjusting the finger status including force, angle, and direction. Multi-group tests have been done on grasping several objects of different sizes and materials in daily life. The relationship between force, bending, and surface material is also analyzed and compared under different conditions. The numerical comparisons related to the measurement error are analyzed based on their standard deviations. Experimental results indicate that this flexible manipulator with proposed system and strategy has better grasping ability for fragile fruits with its good flexibility and dexterity.

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“…R OBOTIC object grasping and manipulation are commonplace in industrial manufacturing. For high-throughput assembly lines, fast and accurate grasping is typically established by custom-made bulk feeders [1] and standard parallel gripper mechanisms [2]. However, these solutions are unsuitable in custom manufacturing, which requires flexibility and reconfigurability following task changes.…”

Section: Introductionmentioning

confidence: 99%

Soft Robotic Gripper With Compliant Cell Stacks for Industrial Part Handling

Netzev

Angleraud

Pieters

2020

IEEE Robot. Autom. Lett.

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Robot object grasping and handling requires accurate grasp pose estimation and gripper/end-effector design, tailored to individual objects. When object shape is unknown, cannot be estimated, or is highly complex, parallel grippers can provide insufficient grip. Compliant grippers can circumvent these issues through the use of soft or flexible materials that adapt to the shape of the object. This paper proposes a 3D printable soft gripper design for handling complex shapes. The compliant properties of the gripper enable contour conformation, yet offer tunable mechanical properties (i.e., directional stiffness). Objects that have complex shape, such as non-constant curvature, convex and/or concave shape can be grasped blind (i.e., without grasp pose estimation). The motivation behind the gripper design is handling of industrial parts, such as jet and Diesel engine components. (Dis)assembly, cleaning and inspection of such engines is a complex, manual task that can benefit from (semi-)automated robotic handling. The complex shape of each component, however, limits where and how it can be grasped. The proposed soft gripper design is tunable by compliant cell stacks that deform to the shape of the handled object. Individual compliant cells and cell stacks are characterized and a detailed experimental analysis of more than 600 grasps with seven different industrial parts evaluates the approach.

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A statistical review of industrial robotic grippers

Cited by 126 publications

References 12 publications

Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

Neuromorphic Event-Based Slip Detection and Suppression in Robotic Grasping and Manipulation

An Integrated Multi-Sensor Network for Adaptive Grasping of Fragile Fruits: Design and Feasibility Tests

Soft Robotic Gripper With Compliant Cell Stacks for Industrial Part Handling

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