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Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast, traditional rigid robots offer high accuracy and repeatability but lack the flexibility of soft robots. We argue that combining these characteristics in a hybrid robotic platform can significantly enhance overall capabilities. This work presents a novel hybrid robotic platform that integrates a rigid manipulator with a fully developed soft arm. This system is equipped with the intelligence necessary to perform flexible and generalizable tasks through imitation learning autonomously. The physical softness and machine learning enable our platform to achieve highly generalizable skills, while the rigid components ensure precision and repeatability., Comment: Corrected missing citation

Soft robotics technology can aid in achieving United Nations Sustainable Development Goals (SDGs) and the Paris Climate Agreement through development of autonomous, environmentally responsible machines powered by renewable energy. By utilizing soft robotics, we can mitigate the detrimental effects of climate change on human society and the natural world through fostering adaptation, restoration, and remediation. Moreover, the implementation of soft robotics can lead to groundbreaking discoveries in material science, biology, control systems, energy efficiency, and sustainable manufacturing processes. However, to achieve these goals, we need further improvements in understanding biological principles at the basis of embodied and physical intelligence, environment-friendly materials, and energy-saving strategies to design and manufacture self-piloting and field-ready soft robots. This paper provides insights on how soft robotics can address the pressing issue of environmental sustainability. Sustainable manufacturing of soft robots at a large scale, exploring the potential of biodegradable and bioinspired materials, and integrating onboard renewable energy sources to promote autonomy and intelligence are some of the urgent challenges of this field that we discuss in this paper. Specifically, we will present field-ready soft robots that address targeted productive applications in urban farming, healthcare, land and ocean preservation, disaster remediation, and clean and affordable energy, thus supporting some of the SDGs. By embracing soft robotics as a solution, we can concretely support economic growth and sustainable industry, drive solutions for environment protection and clean energy, and improve overall health and well-being.

Ionic Polymer Metal Composites (IPMCs) are one of the most preferred choices among biocompatible materials for industrial and biomedical applications. Despite their advantages, some of their drawbacks include non-linear and hysteretic behavior, which complicates the modeling process. In previous works, usually autoregressive models were used to predict the behavior of an IPMC actuator. The main drawback of using an autoregressive model is that it cannot be used in mobile and real-time applications. In this study, we proposed a hybrid analytical intelligent model for an IPMC actuator. The most outstanding feature of this model is its non-autoregressive structure. The hybrid concept proposed in this study can be generalized to various problems other than IPMCs. The structure used in this work comprises an analytical model and a deep neural network, providing a non-linear, dynamic, and non-autoregressive model for the IPMC actuator. Lastly, the average NMSE achieved using the proposed hybrid model is 9.5781e-04 showing a significant drop in the error rate compared to other non-autoregressive structures.

This work presents a new fiberless soft pneumatic actuator that can work multifunctional and bidirectional, and its embedded sensors give it a self-proprioception ability. This actuator works based on the idea of employing helical pressure channels. Applying the controlled input pressures into these two channels causes a variety of deformations and actuation. In particular, single pressure, imbalanced pressures, and balanced pressures applied in the channels cause bidirectional coilings, opposite bendings, and elongation, respectively, in a single unit actuator. Also, two U-shaped microchannels are created, and by injecting a gel-based conductive material, the actuator is equipped with resistive sensors which are responsive to a vast dynamic range from a small oscillation to a large elongation. This actuator has so many promising features as a multifunctional soft gripper, and its embedded soft sensors enable it to have better controllability in real problems. The multifunctionality of this actuator has been validated with several experimental tests, and also we have shown it has excellent potential in gripping a variety of objects. Finally, the embedded sensors can discriminate the main functions of actuators, and also they can play the role of independent sensors as well like a stretch, pressure, or bending sensors.

In this paper, a novel fully 3D, microfluidic-oriented, gel-based, and low-cost highly stretchable resistive sensors have been presented. By the proposed method we are able to measure and discriminate all of the stretch, twist, and pressure features by a single sensor which is the potential that we have obtained from the fully 3D structure of our sensor. Against previous sensors which all have used EGaIn as the conductive material of their sensor, we have used low-cost, safe, and ubiquitous glycol-based gel instead. To show the functionality of the proposed sensor some FEM simulations, a set of the designed experimental tests were done which showed the linear, accurate, and durable operation of the proposed sensor. Finally, the sensor was put through its paces on the knee, elbow, and wrist of a female test subject. Also, to evaluate the pressure functionality of the sensor, a fully 3D active foot insole was developed, fabricated, and evaluated. All of the results show promising features for the proposed sensor to be used in real-world applications like rehabilitation, wearable devices, soft robotics, smart clothing, gait analysis, AR/VR, etc.

3D and 4D printing are cutting-edge technologies for precise and expedited manufacturing of objects ranging from plastic to metal. Recent advances in 3D and 4D printing technologies in dentistry and maxillofacial surgery enable dentists to custom design and print surgical drill guides, temporary and permanent crowns and bridges, orthodontic appliances and orthotics, implants, mouthguards for drug delivery. In the present review, different 3D printing technologies available for use in dentistry are highlighted together with a critique on the materials available for printing. Recent reports of the application of these printed platformed are highlighted to enable readers appreciate the progress in 3D/4D printing in dentistry., Comment: 61 pages, 21 figures, Review Paper

In the present work we study the spider's hair flow-sensing system by using fluid-structure interaction (FSI) numerical simulations. We observe experimentally the morphology of Theraphosa stirmi's hairs and characterize their mechanical properties through nanotensile tests. We then use the obtained information as input for the computational model. We study the effect of a varying air velocity and a varying hair spacing on the mechanical stresses and displacements. Our results can be of interest for the design of novel bio-inspired systems and structures for smart sensors and robotics.

We consider the fully-developed flow of an incompressible Newtonian fluid in a cylindrical vessel with elliptical cross-section (both an ellipse and the annulus between two confocal ellipses). In particular, we address an inverse problem, namely to compute the velocity field associated with a given, time-periodic flow rate. This is motivated by the fact that flow rate is the main physical quantity which can be actually measured in many practical situations. We propose a novel numerical strategy, which is nonetheless grounded on several analytical relations. The proposed method leads to the solution of some simple ordinary differential systems. It holds promise to be more amenable to implementation than previous approaches, which are substantially based on the challenging computation of Mathieu functions. Some numerical results are reported, based on measured data for human blood flow in the internal carotid artery, and cerebrospinal fluid (CSF) flow in the upper cervical region of the human spine. As expected, computational efficiency is the main asset of our solution: a speed-up factor over 10^3 was obtained, compared to more elaborate numerical approaches. The main goal of the present study is to provide an improved source of initial/boundary data for more ambitious numerical approaches, as well as a benchmark solution for pulsatile flows in elliptical sections with given flow rate. The proposed method can be effectively applied to bio-fluid dynamics investigations (possibly addressing key aspects of relevant diseases), to biomedical applications (including targeted drug delivery and energy harvesting for implantable devices), up to longer-term medical microrobotics applications., Comment: 19 pages, 6 figures