Soft Robotics Mechanosensing

Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network

Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment.

Accurate Monitoring of Small Strain for Timbre Recognition via Ductile Fragmentation of Functionalized Graphene Multilayers

Sensitivity and linearity are two key parameters of flexible strain sensors. Although the introduction of microstructures (e.g., channel crack inspired by the geometry of the spider's slit organ) can effectively improve the sensitivity, the sudden breakage of the conductive path in turn leads to poor linearity. In practical applications, in order to achieve precise detection of subtle strains, high sensitivity and high linearity are required simultaneously. Here, we report a strain sensor design strategy based on the ductile fragmentation of functionalized graphene multilayers (FGMs) in which the conductive path is gradually broken to ensure high sensitivity while greatly improving the linear response of the sensor. The presence of oxygen-containing functional groups plays a key role in the deformation and fracture behaviors of the sensitive layer. High sensitivity (gauge factor ∼ 200) and high linearity (adjusted R-square ∼ 0.99936) have been achieved simultaneously in the strain range of 0-2.5%. In addition, the sensor also shows an ultralow detection limit (ε < 0.001%), an ultrafast response (response time ∼ 50 μs), good stability, and good patterning capability compatible with complex curved surface manufacturing. These outstanding performances allow the FGM-based strain sensors to accurately distinguish the sound amplitude and frequency, highlighting the sensor's potential as smart devices for human voice detection. Such sensors have potential applications in the fields of smart skin, wearable electronics, robotics, and so on.

Toward Perceptive Soft Robots: Progress and Challenges

In the past few years, soft robotics has rapidly become an emerging research topic, opening new possibilities for addressing real-world tasks. Perception can enable robots to effectively explore the unknown world, and interact safely with humans and the environment. Among all extero- and proprioception modalities, the detection of mechanical cues is vital, as with living beings. A variety of soft sensing technologies are available today, but there is still a gap to effectively utilize them in soft robots for practical applications. Here, the developments in soft robots with mechanical sensing are summarized to provide a comprehensive understanding of the state of the art in this field. Promising sensing technologies for mechanically perceptive soft robots are described, categorized, and their pros and cons are discussed. Strategies for designing soft sensors and criteria to evaluate their performance are outlined from the perspective of soft robotic applications. Challenges and trends in developing multimodal sensors, stretchable conductive materials and electronic interfaces, modeling techniques, and data interpretation for soft robotic sensing are highlighted. The knowledge gap and promising solutions toward perceptive soft robots are discussed and analyzed to provide a perspective in this field.

Integrated Simultaneous Detection of Tactile and Bending Cues for Soft Robotics

Soft robots should move in an unstructured environment and explore it and, to do so, they should be able to measure and distinguish proprioceptive and exteroceptive stimuli. This can be done by embedding mechanosensing systems in the body of the robot. Here, we present a polydimethylsiloxane block sensorized with an electro-optical system and a resistive strain gauge made with the supersonic cluster beam implantation (SCBI) technique. We show how to integrate these sensing elements during the whole fabrication process of the soft body and we demonstrate that their presence does not change the mechanical properties of the bulk material. Exploiting the position of both sensing systems and a proper combination of the output signals, we present a strategy to measure simultaneously external pressure and positive/negative bending of the body. In particular, the optical system can reveal any mechanical stimulation (external from the soft block or due to its own deformation), while the resistive strain gauge is insensitive to the external pressure, but sensitive to the bending of the body. This solution, here applied to a simple block of soft material, could be extended to the whole body of a soft robot. This approach provides detection and discrimination of the two stimuli (pressure and bending), with low computational effort and without significant mechanical constraint.