A novel multi objective constraints based industrial gripper design with optimized stiffness for object grasping

Dinakaran, Venkatesa Prabu; Balasubramaniyan, Meenakshi Priya; Le, Q.H.; Alrubaie, Ali Jawad; Al‐Kharsan, Ibrahim H.; Muthusamy, Suresh; Panchal, Hitesh; Jaber, Mustafa Musa; Dixit, Anil Kumar; Prakash, Chander

doi:10.1016/j.robot.2022.104303

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Robotic automation has made significant contributions to industries related to consumer goods, logistics, industrial production, and food processing. [1][2][3] In such industrial environments, the reliable robot handling of goods depends to a large extent on the development of reliable, adaptable, and multifunctional grippers. Different from traditional rigid grippers, the most advanced soft grippers are made of compliant soft actuators and functional soft materials, [4][5][6] for example, shape memory alloys (SMAs), silicone, dielectric elastomers, and ionic polymer-metal composites.…”

Section: Introductionmentioning

confidence: 99%

“…Soft grippers are adaptable to various gripping environments, have high flexibility, safe interaction, and passive compliance; therefore, they have great potential for application in industries related to material handling. 1,[7][8][9] Currently, the main driving methods for soft actuators include fluid-driven, 10 tendon-driven, [11][12][13] shape memory alloy-driven, [14][15][16][17] and electroactive polymer-driven systems. 18 Among them, soft pneumatic actuators (SPAs) have been extensively used due to their advantages of small size, high safety, low cost, and ease of manufacture.…”

Section: Introductionmentioning

confidence: 99%

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Design and motion analysis of a bidirectional parallel-chamber soft pneumatic actuator

Wu,

Lu,

Ding

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This article presents a novel bidirectional parallel-chamber soft actuator that improves the end-effector grasping force by adding a row of chambers at both ends of its longitudinal chambers. A mathematical model of the bending behavior of the longitudinal chambers of the actuator is established using the piecewise constant curvature assumption, and the bending properties, gripping force, and contact area of the end wrap were analyzed for the longitudinal and transverse chambers of the actuator. Compared to traditional soft actuators, the proposed bidirectional parallel-chamber actuator provides 1.78 N of grasping force at the end due to longitudinal bending, and 1 N of grasping force at the tail due to transverse bending. To validate the simulation results, a testing platform is built to measure the bending angle and output force of the actuator experimentally. Finally, a two-fingered soft gripper is developed to verify the mathematical model and simulation results, and is tested in grasping objects with diameters ranging from 0 to 250 mm and weights up to 1450 g.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and motion analysis of a bidirectional parallel-chamber soft pneumatic actuator

Wu,

Lu,

Ding

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Object grasping is a crucial task for robots with applications in manufacturing, logistics, healthcare, and household tasks [10,25]. However, detecting and segmenting objects accurately in the robot's environment is challenging due to occlusions, complex geometries, and dynamic backgrounds [7]. Panoptic segmentation aims to simultaneously segment foreground objects and background regions in an image.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

Kachole,

Alkendi,

Baghaei Naeini

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code

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Robotics as key enabler technology in Food Industry 4.0 and beyond

Raheem,

Treiblmaier,

Mohammed

et al. 2024

Food Industry 4.0

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A novel multi objective constraints based industrial gripper design with optimized stiffness for object grasping

Cited by 6 publications

References 28 publications

Design and motion analysis of a bidirectional parallel-chamber soft pneumatic actuator

Design and motion analysis of a bidirectional parallel-chamber soft pneumatic actuator

Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network

Robotics as key enabler technology in Food Industry 4.0 and beyond

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