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American Society for Testing and Materials

Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future.

25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress

Starting from human ?sense of touch,? this paper reviews the state of tactile sensing in robotics. The physiology, coding, and transferring tactile data and perceptual importance of the ?sense of touch? in humans are discussed. Following this, a number of design hints derived for robotic tactile sensing are presented. Various technologies and transduction methods used to improve the touch sense capability of robots are presented. Tactile sensing, focused to fingertips and hands until past decade or so, has now been extended to whole body, even though many issues remain open. Trend and methods to develop tactile sensing arrays for various body sites are presented. Finally, various system issues that keep tactile sensing away from widespread utility are discussed.

Tactile Sensing—From Humans to Humanoids

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201707035

Soft Robotic Grippers.

This paper provides a broad overview of medical robot systems used in surgery. After introducing basic concepts of computer-integrated surgery, surgical CAD/CAM, and surgical assistants, it discusses some of the major design issues particular to medical robots. It then illustrates these issues and the broader themes introduced earlier with examples of current surgical CAD/CAM and surgical assistant systems. Finally, it provides a brief synopsis of current research challenges and closes with a few thoughts on the research/industry/clinician teamwork that is essential for progress in the field.

/pdf/medical-robotics-in-computer-integrated-surgery-551rsaf1dd.pdf

Medical robotics in computer-integrated surgery

Evaluation of mechanical interlock effect on adhesion strength of polymer–metal interfaces using micro-patterned surface topography

Damage detection of CFRP laminates using electrical resistance measurement and neural network

This paper describes two kinds of 3 × 3 force sensor arrays using fiber Bragg gratings (FBG) and transducers for tactile sensation to detect a distributed normal force. One array is developed for a large area tactile sensor that has good sensitivity but low spatial resolution, similar to human body skin. The other is for a small area tactile sensor that has good sensitivity and spatial resolution, similar to human finger skin. The transducer is designed such that it is not affected by chirping and light loss. We also present the fabrication process and experimental verification of the prototype sensors. Experimental tests show that the newly designed sensors have good performance: good sensitivity, repeatability, and no-hysteresis. The load calibration is accomplished by a verified uniaxial load cell. In order to provide a more precise measurement, temperature compensation is applied to all taxels. These force sensor arrays are flexible enough to be attached to a curved surface and they also have simple wiring compared with other types of small force sensors for tactile sensation.

Jung-Ju Lee

Papers

Evaluation of mechanical interlock effect on adhesion strength of polymer–metal interfaces using micro-patterned surface topography

Damage detection of CFRP laminates using electrical resistance measurement and neural network

Tactile sensor arrays using fiber Bragg grating sensors

Axial crush and bending collapse of an aluminum/GFRP hybrid square tube and its energy absorption capability

Determination of cohesive parameters for a mixed-mode cohesive zone model