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Simulation is a standard tool for robot design, control, and performance analysis. Numerous mature, thoroughly validated methods exist to build fast and reliable simulation models for traditional rigid robotics platforms. However, when it comes to novel soft robotics systems, most existing models concern themselves with the dynamics of the soft bodies during actuation, largely disregarding the sensory system. Simultaneous simulation of sensors and actuators is essential to establishing a realistic and accurate robot model in the virtual environment. This article proposes a pipeline to implement coupling field simulation (CFS) of capacitive sensors deployed on a square soft arm and a pneumatic manipulator. The CFS approach can seamlessly integrate mechanical and sensing components, enabling us to understand sensor behavior better by simulating sensor responses to various deformations including bending, inflation, and the combinations of bending, twisting, and elongation. We also demonstrate that CFS is cost-effective manner to acquire a large number of annotated data that can be used for pretraining in soft robot perception tasks through two case studies (i.e., applied force estimation and deformation classification).

Prokaryotic flagellum is considered as the only known example of a biological “wheel,” a system capable of converting the action of rotatory actuator into a continuous propulsive force. For this reason, flagella are an interesting case study in soft robotics and they represent an appealing source of inspiration for the design of underwater robots. A great number of flagellum-inspired devices exists, but these are all characterized by a size ranging in the micrometer scale and mostly realized with rigid materials. Here, we present the design and development of a novel generation of macroscale underwater propellers that draw their inspiration from flagellated organisms. Through a simple rotatory actuation and exploiting the capability of the soft material to store energy when interacting with the surrounding fluid, the propellers attain different helical shapes that generate a propulsive thrust. A theoretical model is presented, accurately describing and predicting the kinematic and the propulsive capabilities of the proposed solution. Different experimental trials are presented to validate the accuracy of the model and to investigate the performance of the proposed design. Finally, an underwater robot prototype propelled by four flagellar modules is presented.

Multiphase flow is ubiquitous in nature, industry and research, and accurate flow imaging is critical to understanding this complex phenomenon. Electrical tomography (ET) is a promising technique for multiphase flow visualization and characterization which provides a non-invasive and non-radiative way to unravel the internal physical properties at high temporal resolution. However, existing ET-based multiphase flow imaging methods are inadequate for quantitative imaging of multiphase flows due to inversion errors and limited ground truth data of fluid phases distribution. Here we report a digital twin (DT) framework of ET to address the challenges of real-time quantitative multiphase flow imaging. The proposed DT framework, building upon a synergistic integration of 3D field coupling simulation, model-based deep learning, and edge computing, allows ET to dynamically learn the flow features in the virtual space and implement the model in the physical system, thus providing excellent resolution and accuracy. The proposed DT framework is demonstrated using electrical capacitance tomography (ECT) of a gas-liquid two-phase flow. It can be readily extended to a broader range of tomography modalities, scenarios, and scales in biomedical, energy, and aerospace applications.

Pulsatile flow is widespread in nature, but replicating such impulsive periodic pumping routines with traditional rotary actuators is complex and energetically inefficient. We demonstrate the feasibility of an actuator capable of generating impulsive flow displacement by exploiting the bistable equilibrium of a simple mechanism. These kind of mechanisms, commonly known as snap-through mechanisms, offer the benefit of sudden release of elastic energy at the interface between two stable structural configurations. This property has been employed in certain actuators to drive abrupt motions of mechanical systems. Here, we use this principle to drive an inflation/deflation routine of a fluid-filled cavity, thus generating a peaked, pulsatile flow, comparable to that encountered in biological systems. Assessment of the various stages of actuation of this system shows that a sharp drop in the energy occurs from elastic to hydraulic work, highlighting the need for improved design elements involved in this stage of the actuation.

Proprioception, the ability to perceive posture, position, and movement, is a prerequisite for accurate body control in animals and robots. The lack of three-dimensional full-body proprioceptive sense has become a key constraint for fundamental breakthroughs in bioinspired soft robots. Here, we report a type of flexible capacitive e-skins to endow soft robots with high-resolution, full-geometry bodily awareness beyond existing proprioception systems. We demonstrate that our intrinsically-stretchable e-skin can capture complex three-dimensional deformations across the entire soft body through recursive, multi-position capacitance measurements. The e-skin signals can be directly translated to high-density point clouds portraying the complete geometry via the deep net. This augmented proprioception system providing millimeter-scale, global sensory-motor awareness can offer a crucial solution to fundamental problems in soft robotics, such as enabling precise closed-loop control and developing digital twins of soft robots.

The aim of this work was to assess corneal endothelial morphology in a well-established acute graft-versus-host disease (GVHD) murine model and to quantify the expression of neurokinin-1 receptor (NK1R) in the corneal endothelium during ocular GVHD (oGVHD). Pre-conditioning was performed in BALB/c using myeloablative total body irradiation. Subsequently, allogeneic bone marrow transplantation was performed without (BM) or with mature T cells (BM + T). Corneal transparency was monitored with in vivo biomicroscopy. After sacrifice, corneal thickness and endothelial cell number were measured, and the expression of NK1R was investigated in the corneal endothelium through immunofluorescence and quantified by immunohistochemistry. Mice presenting oGVHD showed a significant reduction in endothelial cell number compared to control animals (p 0.0001). NK1R expression was significantly increased in oGVHD mice endothelium (p 0.05). Corneal transparency and thickness were unchanged in all groups. Our results suggest that oGVHD affects the corneal endothelium, inducing a reduction of the cell number, and that this is associated with increased expression of the pro-inflammatory marker NK1R.

This paper reports a new approach to quantitatively evaluate the performance of Electrical Tomography (ET) in measuring dynamic multiphase flows. A virtual multiphase flow imaging platform based on ET is constructed and demonstrated on two typical gas–liquid flows, i.e., water–gas flow and oil–gas flow. Two coupling simulation cases, i.e., water–gas flow field and electric currents field coupling simulation and oil–gas flow field and electrostatics field coupling simulation, are performed to simulate multiphase flow sensing of Electrical Impedance Tomography (EIT) and Electrical Capacitance Tomography (ECT). We quantitatively evaluated the representative EIT and ECT image reconstruction algorithms on the virtual evaluation platform bringing evidence of the improved capability to capture the key flow features of the fluid mixture with respect to traditional static phantoms. Ad-hoc treatment of the signal noise enables one to better capture dynamic responses of the fluid phase volume fractions and their spatial gradients throughout their mixing along the conduit, ultimately demonstrating unprecedented potential in the quantitative characterization of complex, unsteady multi-phase systems. The proposed image reconstruction constitutes a highly effective platform for quantitative performance evaluation of ET, parameter optimization of model-based ET image reconstruction algorithms, and for the development of data-driven ET algorithms in multiphase flow imaging.

In response to the pervasive anthropogenic pollution of the ocean, this manuscript suggests the use of biodegradable elastomers in marine applications. The present study characterizes 25 samples of highly biodegradable polymers, obtained blending a base elastomer with natural fibers. Mechanical analysis and Scanning Electron Microscope imaging, reveal how base polymers behave differently depending on the plant fiber chosen, on the external forcing—exposure to water—and on the doses that constitute the final biocomposite. Results suggest that EcoflexTM 00-30 and EcoflexTM 00-50, mixed with potato starch, perform best mechanically, maintaining up to 70% of their maximum tensile strain. Moreover, early signs of degradation are visible on polysiloxane rubber blended with 50% vegetable fibers after 19 hours in distilled water. Analyses demonstrate that highly biodegradable elastomers are good candidates to satisfy the requirements of aquatic devices. Furthermore, the discussed materials can improve the dexterity and biodegradability of marine technology.

This systematic review and meta-analysis investigated the role of robotic-assisted surgery in patients undergoing revisional bariatric surgery (RBS). According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search of PubMed, Cochrane Library, Web of Science, and Google Scholar was performed: ((“sleeve”AND “gastr*”)OR “bariatric”OR “gastric bypass”)AND(“robot*”OR “DaVinci”OR “Da Vinci”)AND(“revision*”OR “conversion*”). In this review, six studies with 29,890 patients were included (2459 in the robotic group). No difference in postoperative complications (RR 1.070, 95%CI 0.930–1.231, p = 0.950), conversions to open surgery (RR 1.339, 95%CI 0.736–2.438, p = 0.339), length of stay (SMD − 0.041, 95%CI − 0.420–0.337, p = 0.831) or operative time (RR 0.219, 95%CI − 0.539–0.977, p = 0.571) was found. This systematic review and meta-analysis showed no significant advantage of robotic-assisted RBS; on the other hand, it showed a non-inferior efficacy compared to standard laparoscopy.

Shallow water environments pose daunting scenarios for the operation of Unmanned Underwater Vehicles (UUVs), due to significantly larger wave disturbances being present in comparison to a typical deep sea situation. Performing inspection and maintenance tasks at close quarters in these conditions requires reliable control methods robust to external disturbances, allowing accurate position and attitude control, an aspect which classical control methods are often lacking. Improved performance can be achieved through predictive control methods, however, these require accurate and time-efficient estimations of the hydrodynamic forces produced by the immediate ocean environment around the vehicle. Considering this, we present a low-order model for faster-than-real time estimation of the wave-induced hydrodynamic forces acting on a submerged vehicle in various sea state conditions. The model is thoroughly corroborated by experimental tests, performed using a Remotely Operated Vehicle (ROV) situated at shallow depth whilst subjected to realistic sea wave disturbances. Validation between simulations and the collected experimental data showed a maximum normalised mean error deviation of 0.16 and 0.27 for surge and heave forces respectively, and 0.34 for the pitching moment. This empirical evidence demonstrates that accurate predictions of wave-generated forces can be produced through low-order models at a speed suitable for incorporation within predictive control architectures.