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The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans’ examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.

 

Packaging electronic devices within electronic textiles and fibrous substrates requires an understanding of how fibers interact with circuit components in different operating conditions. In this paper, we use microeletromechanical (MEMS) devices to put devices in electrical contact with fine wires. We characterize the electronic properties of MEMS-to-wire contacts and discuss general guidelines for optimizing the design of these grippers and potential MEMS-based circuits. We then demonstrate how these grippers can act as non-rigid circuit components that effectively transfer power to devices such as LEDs. Analysis shows that our grippers are suitable conductors (under 150 Ohms) under standard operating temperatures (25-100 deg. C) with potential for use as sensors for current overflow or temperature. Methods such as parylene deposition and silver epoxy to stabilize MEMS performance are briefly discussed and explored. 

The mechanoreceptors of the human tactile sensory system contribute to natural grasping manipulations in everyday life. However, in the case of robot systems, attempts to emulate humans’ dexterity are still limited by tactile sensory feedback. In this work, a soft optical lightguide is applied as an afferent nerve fiber in a tactile sensory system. A skin‐like soft silicone material is combined with a bristle friction model, which is capable of fast and easy fabrication. Due to this novel design, the soft sensor can provide not only normal force (up to 5 Newtons) but also lateral force information generated by stick‐slip processes. Through a static force test and slip motion test, its ability to measure normal forces and to detect stick‐slip events is demonstrated. Finally, using a robotic gripper, real‐time control applications are investigated where the sensor helps the gripper apply sufficient force to grasp objects without slipping.

 

Electronic textile (E-textile) research requires an understanding of the mechanical properties of fabric substrates used to build and support electronics. Because fibers are often non-uniform and fabrics are easily deformed, locating fiber junctions on the irregular surface is challenging, yet is essential for packaging electronics on textiles at the resolution of single fibers that deliver power and signals. In this paper, we demonstrate the need to identify fiber junctions in a task where microelectromechanical structures (MEMS) are integrated on fabrics. We discuss the benefits of fiber-aligned placement compared with random placement. Thereafter we compare three image processing algorithms to extract fiber junction locations from sample fabric images. The Hough line transform algorithm implemented in MATLAB derives line segments from the image to model the fibers, identifying crossings by the intersections of the line segments. The binary image analysis algorithm implemented in MATLAB searches the image for unique patterns of 1s and 0s that represent the fiber intersection. The pattern matching algorithm implemented in Vision Assistant - LabVIEW, uses a pyramid value correlation function to match a reference template to the remainder of the fabric image to identify the crossings. Of the three algorithms, the binary image analysis method had the highest accuracy, while the pattern matching algorithm was fastest. 

ABSTRACT This paper discusses new components and approaches to make stretchable optical fiber sensors better meet the power and washability requirements of wearables. First, an all-polymer quick connector allows the light source and photosensor to be quickly detached for washing. Second, the paper investigates the possibility of driving the sensors using ambient light instead of an onboard light source. While optical strain sensors and touch sensors have advantages over electronic ones in wet environments, and the intrinsic stretchability of the fibers is useful for soft robotics and highly conformal wearables, the typical light-emitting diode (LED) light source consumes more power than an electronic resistive or capacitive strain sensor. In this work, ambient light of uniform but unknown intensity is demonstrated to drive an elastomeric optical touch sensor in a differential configuration.