Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation

Alspach, Alex; Hashimoto, Kazuaki; Kuppuswarny, Naveen; Tedrake, Russ

doi:10.1109/robosoft.2019.8722713

Cited by 87 publications

(52 citation statements)

References 25 publications

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“…In [37], the change in diameter of the markers was used to measure the movement in the z direction. In [73,74], a depth camera is used to measure the shape of the soft fingertip, including the z direction. The marker is not embedded in the soft fingertip body, but the depth camera projects patterns on the inside of the sensor skin, and the principle is basically the same in this category.…”

Section: Physical Contact To Light Conversionmentioning

confidence: 99%

Tactile Image Sensors Employing Camera: A Review

Shimonomura

2019

Sensors

128

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A tactile image sensor employing a camera is capable of obtaining rich tactile information through image sequences with high spatial resolution. There have been many studies on the tactile image sensors from more than 30 years ago, and, recently, they have been applied in the field of robotics. Tactile image sensors can be classified into three typical categories according to the method of conversion from physical contact to light signals: Light conductive plate-based, marker displacement- based, and reflective membrane-based sensors. Other important elements of the sensor, such as the optical system, image sensor, and post-image analysis algorithm, have been developed. In this work, the literature is surveyed, and an overview of tactile image sensors employing a camera is provided with a focus on the sensing principle, typical design, and variation in the sensor configuration.

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Section: Physical Contact To Light Conversionmentioning

confidence: 99%

Tactile Image Sensors Employing Camera: A Review

Shimonomura

2019

Sensors

128

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“…A tactile sensor employing a three-color camera can obtain detailed tactile information with a high spatial resolution (Kamiyama et al, 2004). Other advanced imaging system technologies, i.e., depth cameras (Alspach et al, 2019) and dynamic vision sensors (Baghaei Naeini et al, 2020), have been employed for the same goal of tracking externally induced visual features and providing high-resolution information about the deformation of a soft elastomeric surface. In addition, event-based cameras can measure contrast change in time and give a reading of intensity temporal change at each pixel (Kumagai and Shimonomura, 2019).…”

Section: Imaging-based Tactile Sensingmentioning

confidence: 99%

Tactile Sensing for Minimally Invasive Surgery: Conventional Methods and Potential Emerging Tactile Technologies

Othman¹,

Lai²,

Abril³

et al. 2022

Front. Robot. AI

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As opposed to open surgery procedures, minimally invasive surgery (MIS) utilizes small skin incisions to insert a camera and surgical instruments. MIS has numerous advantages such as reduced postoperative pain, shorter hospital stay, faster recovery time, and reduced learning curve for surgical trainees. MIS comprises surgical approaches, including laparoscopic surgery, endoscopic surgery, and robotic-assisted surgery. Despite the advantages that MIS provides to patients and surgeons, it remains limited by the lost sense of touch due to the indirect contact with tissues under operation, especially in robotic-assisted surgery. Surgeons, without haptic feedback, could unintentionally apply excessive forces that may cause tissue damage. Therefore, incorporating tactile sensation into MIS tools has become an interesting research topic. Designing, fabricating, and integrating force sensors onto different locations on the surgical tools are currently under development by several companies and research groups. In this context, electrical force sensing modality, including piezoelectric, resistive, and capacitive sensors, is the most conventionally considered approach to measure the grasping force, manipulation force, torque, and tissue compliance. For instance, piezoelectric sensors exhibit high sensitivity and accuracy, but the drawbacks of thermal sensitivity and the inability to detect static loads constrain their adoption in MIS tools. Optical-based tactile sensing is another conventional approach that facilitates electrically passive force sensing compatible with magnetic resonance imaging. Estimations of applied loadings are calculated from the induced changes in the intensity, wavelength, or phase of light transmitted through optical fibers. Nonetheless, new emerging technologies are also evoking a high potential of contributions to the field of smart surgical tools. The recent development of flexible, highly sensitive tactile microfluidic-based sensors has become an emerging field in tactile sensing, which contributed to wearable electronics and smart-skin applications. Another emerging technology is imaging-based tactile sensing that achieved superior multi-axial force measurements by implementing image sensors with high pixel densities and frame rates to track visual changes on a sensing surface. This article aims to review the literature on MIS tactile sensing technologies in terms of working principles, design requirements, and specifications. Moreover, this work highlights and discusses the promising potential of a few emerging technologies towards establishing low-cost, high-performance MIS force sensing.

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“…One category of optical tactile sensors uses a camera to track sparse markers within a soft, transparent gel, which deforms when subject to external forces, see for example [15], [16]. Other optical devices are able to provide information about the contact with the environment, as shown with the use of dynamic vision sensors [17] and depth cameras [18]. The sensor used here for the evaluation of the proposed approach is based on a standard camera (which retains a small size) that tracks a dense spread of particles within a soft gel, see for example Fig.…”

Section: A Related Workmentioning

confidence: 99%

Ground Truth Force Distribution for Learning-Based Tactile Sensing: A Finite Element Approach

et al. 2019

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Skin-like tactile sensors provide robots with rich feedback related to the force distribution applied to their soft surface. The complexity of interpreting raw tactile information has driven the use of machine learning algorithms to convert the sensory feedback to the quantities of interest. However, the lack of ground truth sources for the entire contact force distribution has mainly limited these techniques to the sole estimation of the total contact force and the contact center on the sensor's surface. The method presented in this article uses a finite element model to obtain ground truth data for the three-dimensional force distribution. The model is obtained with state-of-the-art material characterization methods and is evaluated in an indentation setup, where it shows high agreement with the measurements retrieved from a commercial force-torque sensor. The proposed technique is applied to a vision-based tactile sensor, which aims to reconstruct the contact force distribution purely from images. Thousands of images are matched to ground truth data and are used to train a neural network architecture, which is suitable for real-time predictions.

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Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation

Cited by 87 publications

References 25 publications

Tactile Image Sensors Employing Camera: A Review

Tactile Image Sensors Employing Camera: A Review

Tactile Sensing for Minimally Invasive Surgery: Conventional Methods and Potential Emerging Tactile Technologies

Ground Truth Force Distribution for Learning-Based Tactile Sensing: A Finite Element Approach

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