GelTip tactile sensor for dexterous manipulation in clutter

Gomes, Daniel Fernandes; Luo, Shan

doi:10.1016/b978-0-32-390445-2.00008-8

Cited by 8 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, the deforming surface (soft sensing surface) has markers or pins arranged on its inner surface, whose displacements are recorded by the camera [32,[56][57][58][59][60][61]. Apart from using pins or markers, the imprints of the external object on the sensing skin are captured by the camera in certain sensors [62][63][64][65][66][67][68]. Compared to other transduction methods discussed above, the camerabased method calls for a bigger form factor of the sensor, as it has to house a camera with its illumination provisions and also needs to place the camera away from the sensing surface to obtain a better view of the surface.…”

Section: Camera/vision Basedmentioning

confidence: 99%

“…It is represented in terms of coordinate values/area relative to the sensor surface. The sensors using transduction methods like EIT [55], magnetic [90], acoustic [91], camera-based [58,63,64], and a few other combinations (EIT-acoustics [80], piezoresisitve-Hall effect [78], triboelectricpiezoelectric-pyroelectric [79]) are reported to generate contact location information.…”

Section: Contact Locationmentioning

confidence: 99%

See 1 more Smart Citation

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Mandil,

Rajendran,

Nazari

et al. 2023

Sensors

View full text Add to dashboard Cite

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems.

show abstract

Section: Camera/vision Basedmentioning

confidence: 99%

Section: Contact Locationmentioning

confidence: 99%

Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation

Mandil,

Rajendran,

Nazari

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Artificial Skin Based on Visuo‐Tactile Sensing for 3D Shape Reconstruction: Material, Method, and Evaluation

Zhang,

Yang,

Sun

et al. 2024

Adv Funct Materials

View full text Add to dashboard Cite

Artificial skin has shown great potential in robot perception and human healthcare. It provides multifunctional tactile sensing, including 3D shape reconstruction, contact feedback, and temperature perception, where the 3D reconstruction function is indispensable for dexterous hands in tactile cognition and interaction. Vision‐based tactile sensor (VTS) is an innovative bionic tactile sensor and supports high‐resolution, high‐precision, and high‐density tactile reconstruction compared with electronic tactile sensors. Considering the unique contribution of visuo‐tactile sensing to artificial skin, this review focuses on the 3D reconstruction techniques of the VTS. 3D reconstruction methods are classified into five categories based on sensing modalities, hardware categories, and modeling approaches: 1) photometric stereo, 2) binocular depth calibration, 3) optical flow, 4) deep learning, and 5) ToF (time of flight). In addition, the association and difference of reconstruction methods are analyzed from the hardware perspective, and the development and technological details of 3D reconstruction are summarized. On this basis, the challenges and development direction are discussed. This review can be viewed as a technology guide to provide references for interested researchers. Furthermore, it is expected to promote the extensive application of the VTS in artificial skins.

show abstract