Eagle Shoal: A new designed modular tactile sensing dexterous hand for domestic service robots

Wang, Tao; Geng, Zhanxiao; Kang, Bo Gyeong; Luo, Xiaochuan

doi:10.1109/icra.2019.8793842

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…The anatomy of the human hands, the deformability of the skin and muscle, and their complex cognitive functions define their holding procedures and contact forces, resulting in highly developed grasping ability. Based on the efficient grasping characteristics of the human hands, various robot hands have been recently developed to improve object manipulation efficiency. − These robot hands can be categorized into two groups: adaptive grippers (dexterous robot hands) and parallel grippers. Adaptive grippers comprise many joints, which can not only skillfully handle various objects and grasp objects similar to human hands, but also undertake human-like motions, such as handshakes and sign languages.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to control adaptive grippers precisely because those robot hands include many actuators and sensors. Furthermore, the system is highly complex and incurs a high production cost. − Meanwhile, parallel grippers have a simple system structure and are easy to be controlled because they have fewer actuators and sensors than adaptive grippers. , However, these advantages cause poor flexibility in the gripper, resulting in limited shapes of target objects that the gripper can grasp.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beyond Human Hand: Shape-Adaptive and Reversible Magnetorheological Elastomer-Based Robot Gripper Skin

Choi

Kim

Lee

et al. 2020

ACS Appl. Mater. Interfaces

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Developing a simple and universal solution for gripping fragile, multiscaled, and arbitrary-shaped objects using a robot gripper is challenging. Herein, we propose a universal, shapeadaptive/-retaining and reversible, hardness-variable gripper skin that serves as a resourceful solution for grasping such objects without damaging them. The proposed universal gripper skin based on a magnetorheological elastomer is attached to a robot gripper. The proposed skin takes the shape of a target object as soon as the gripper grasps the object. At this time, we solidify the gripper skin by applying a magnetic field, thereby allowing the gripper to grasp the target object easily. After releasing the objects, the magnetic field is removed and the deformed proposed gripper skin rapidly restores its original shape. The proposed adaptive gripper skin is made to grasp various target objects, such as cylinders, cuboids, and triangular prisms, based on which its grasping performance is evaluated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beyond Human Hand: Shape-Adaptive and Reversible Magnetorheological Elastomer-Based Robot Gripper Skin

Choi

Kim

Lee

et al. 2020

ACS Appl. Mater. Interfaces

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“…Tomo et al [40] integrated a magnetic silicone tactile detection unit on the palm of a robotic hand. Wang et al [41] placed four contacting points on the palm of the designed service robot. Pastor et al [42] integrated a high‐resolution tactile array containing 1400 tactile sensing units on the palm of a robotic hand, which collaboratively worked with the under‐actuated fingers.…”

Section: Embodied Tactile Sensingmentioning

confidence: 99%

“…(B) Reproduced with permission from Ref. [41], ©IEEE, 2019. (C) Reproduced with permission from Ref.…”

Section: Embodied Tactile Sensingmentioning

confidence: 99%

Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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“…The modular design can be used in the structure inside the device and make it cheaper, and it can also use for an independent module for easier usage. Some dexterous hands like the Sandia Hand and the Eagle Shoal take the finger as the basic module, which can mount with the palm easily (Quigley et al , 2014), (Wang et al , 2019a).…”

Section: Introductionmentioning

confidence: 99%

DoraHand: a novel dexterous hand with tactile sensing finger module

Wang

Xie²,

Li³

et al. 2022

Self Cite

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Purpose This study aims to introduce the DoraHand, and the basic capability and performance have been verified in this paper. Besides the idea of sharing modular design and sensor design, the authors want to deliver an affordable and practical dexterous hand to the research area to contribute to the robotic manipulation area. Design/methodology/approach This paper introduced the DoraHand, a novel scalable and practical modular dexterous hand, which, adopting modular finger and palm design, fully actuated joint and tactile sensors, can improve the dexterity for robotic manipulation and lower the complexity of maintenance. A series of experiments are delivered to verify the performance of the hand and sensor module. Findings The parameters of the DoraHand are verified and suitable for the research of robotics manipulation area, the sensing capability has been tested with the static experiment and the slip prediction algorithm. And, the advantage of modular design and extensible interface have been verified by the real application. Research limitations/implications The authors continue improving the DoraHand and extend it to more different applications. The authors want to make the DoraHand as a basic research platform in the robotic manipulation area. Practical implications The DoraHand has been sent to more than ten different research institutes for different research applications. The authors continue working on this hand for better performance, easier usage and more affordability. Social implications This kind of dexterous hand can help researchers get rid of complex physical issues and pay more attention to the algorithm part; it can help to make robotic manipulation work more popular. Originality/value The key design in the DoraHand is the modular finger and sensing module. With the special design in mechanical and electrical parts, the authors build reliable hardware and can support the diversity requirement in the robotic manipulation area. The hand with tactile sensing capability can be used in more research and applications with its extensibility.

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Eagle Shoal: A new designed modular tactile sensing dexterous hand for domestic service robots

Cited by 16 publications

References 29 publications

Beyond Human Hand: Shape-Adaptive and Reversible Magnetorheological Elastomer-Based Robot Gripper Skin

Beyond Human Hand: Shape-Adaptive and Reversible Magnetorheological Elastomer-Based Robot Gripper Skin

Embodied tactile perception and learning

DoraHand: a novel dexterous hand with tactile sensing finger module

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