The sense of touch allows individuals to physically interact with and better perceive their environment. Touch is even more crucial for robots, as robots equipped with thorough tactile sensation can more safely interact with their surroundings, including humans. This article describes a recently developed large-scale tactile sensing system for a robotic link, called TacLINK, which can be assembled to form a whole-body tactile sensing robot arm. The proposed system is an elongated structure comprising a rigid transparent bone covered by continuous artificial soft skin. The soft skin of TacLINK not only provides tactile force feedback but can change its form and stiffness by inflation at low pressure. Upon contact with the surrounding environment, TacLINK perceives tactile information through the three-dimensional (3-D) deformation of its skin, resulting from the tracking of an array of markers on its inner wall by a stereo camera located at both ends of the transparent bone. A finite element model (FEM) was formulated to describe the relationship between applied forces and the displacements of markers, allowing detailed tactile information, including contact geometry and distribution of applied forces, to be derived simultaneously, regardless of the number of contacts. TacLINK is scalable in size, durable in operation, and low in cost, as well as being a high-performance system, that can be widely exploited in the design of robotic arms, prosthetic arms, and humanoid robots, etc. This article presents the design, modeling, calibration, implementation, and evaluation of the system.

In this article, we propose a soft eel robot design using soft pneumatic actuators that mimic eel muscles. Four pairs of soft actuators are used to construct the eel robot body. Pulse signals with suitable shifting phases are utilized to control delivery of compressed air to the actuators in sequence to create a sinusoidal wave from head to tail of the robot body. A model of hydrodynamic forces acting on an anguilliform swimmer when moving in fluid was built to estimate the thrust force generated by the robot at different tail beat frequencies. Experimental data revealed that the generated thrust force was positively correlated with the beat frequency. Measured data showed that swimming efficiency depended on both generated thrust force and body posture in situ . At the beat frequency of 1.25 Hz, and air pressure at three segments from head to tail of 65, 50, and 30 kPa, respectively, the eel robot body showed the best cost of transport (COT) of 19.21 with velocity of 10.5 cm/s (or 0.198 body length per second [BL/s]), compared to the other's values of operation frequency and air pressure. We also found that control shifting phase strongly affects the swimming speed and COT. The robot body reached the highest velocity at around 19 cm/s (0.36 BL/s) with the COT of 10.72. Obtained result in this research would contribute to development of soft elongated swimming robot and enhance the knowledge on swimming performance of both robot and natural eels.

Large-scale robotic skin with tactile sensing ability is emerging with the potential for use in close-contact human–robot systems. Although recent developments in vision-based tactile sensing and related learning methods are promising, they have been mostly designed for small-scale use, such as by fingers and hands, in manipulation tasks. Moreover, learning perception for such tactile devices demands a huge tactile dataset, which complicates the data collection process. To address this, this study introduces a multiphysics simulation pipeline, called SimTacLS , which considers not only the mechanical properties of external physical contact but also the realistic rendering of tactile images in a simulation environment. The system utilizes the obtained simulation dataset, including virtual images and skin deformation, to train a tactile deep neural network to extract high-level tactile information. Moreover, we adopt a generative network to minimize sim2real inaccuracy, preserving the simulation-based tactile sensing performance. Last but not least, we showcase this sim2real sensing method for our large-scale tactile sensor ( TacLink ) by demonstrating its use in two trial cases, namely, whole-arm nonprehensile manipulation and intuitive motion guidance, using a custom-built tactile robot arm integrated with TacLink. This article opens new possibilities in the learning of transferable tactile-driven robotics tasks from virtual worlds to actual scenarios without compromising accuracy.

In this paper, we present an analytical method for studying the role of the micro-patterned morphology of soft fingers with wet adhesion in grasping a deformable thin shell. This work originated from a project on a robotic platform for autonomous attachment and removal of a soft contact lens in a "human eye" under a wet environment. In this scenario, a contact lens (hemispherical thin shell) was gripped by a soft-fingered hand in three conditions: inside/outside a liquid environment and when the contact lens was in contact with a convex curved substrate (as an eye). Two fingertip surfaces were compared: a flat surface, and a micropatterned surface inspired by the adhesion mechanism of a tree-frog's toes. The pattern of the latter comprised 3600 square cells, each of size 85μm×85μm interspaced by 15μm. A proposed analytical model was utilized to evaluate the grasp forces in both types of fingertip surface. The forces were subsequently verified by application in grasping a contact lens. The experimental results showed good agreement with the analysis as the micropattern pad decreased the preload and deformation of the shell 1.1-2 times lower than that of the flat surface. This work could be extended to model grasping interfaces with deformable curvature objects in wet and high moisture content environments.

The sensor morphology is of great importance, especially for tactile sensors that mainly transduce physical contacts to perceivable signals for further processing by the central system. Sensors with various morphology can result in different signal thus potentially influencing perception. Most previous researches on tactile sensor morphology focused on revealing advance of a particular morphology in a binary manner, by providing sensors with and without that morphology then comparing the performance. Less attention was paid to sensors with changeable morphology. This letter demonstrates a wrinkled soft tactile sensor with variable morphology, leading to tunable sensing and perception property. Specifically, the geometrical structure of wrinkles and the overall stiffness of the sensor can be varied by changing the sensor bending state. This morphing behavior was modeled analytically and verified by both Finite Element Analysis and real experiment. Furthermore, these morphological variations were found to closely affect the performance of sensor in three tasks including force sensing, shape discrimination and texture detection. Interestingly, for each task, there was always an optimal morphological state that maximized the sensor performance, namely force sensitivity and perception accuracy. This sheds light to novel active tactile sensing system design, thus realizing "active" by adapting morphology instead of sensorimotor control algorithm to optimize perception gain.

There is a growing need for vertical takeoff and landing vehicles, including drones, which are safe to use and can adapt to collisions. The risks of damage by collision, to humans, obstacles in the environment, and drones themselves, are significant. This has prompted a search into nature for a highly resilient structure that can inform a design of propellers to reduce those risks and enhance safety. Inspired by the flexibility and resilience of dragonfly wings, we propose a novel design for a biomimetic drone propeller called Tombo propeller. Here, we report on the design and fabrication process of this biomimetic propeller that can accommodate collisions and recover quickly, while maintaining sufficient thrust force to hover and fly. We describe the development of an aerodynamic model and experiments conducted to investigate performance characteristics for various configurations of the propeller morphology and related properties, such as generated thrust force, thrust force deviation, collision force, recovery time, lift-to-drag ratio, and noise. Finally, we design and showcase a control strategy for a drone equipped with Tombo propellers that collides in midair with an obstacle and recovers from collision continuing flying. The results show that the maximum collision force generated by the proposed Tombo propeller is less than two-thirds that of a traditional rigid propeller, which suggests the concrete possibility to employ deformable propellers for drones flying in a cluttered environment. This research can contribute to the morphological design of flying vehicles for agile and resilient performance.