Soft robotics for infrastructure protection

The paradigm change introduced by soft robotics is going to dramatically push forward the abilities of autonomous systems in the next future, enabling their applications in extremely challenging scenarios. The ability of soft robots to safely interact and adapt to the surroundings is key to operate in unstructured environments, where the autonomous agent has little or no knowledge about the world around it. A similar context occurs when critical infrastructures face threats or disruptions, for examples due to natural disasters or external attacks (physical or cyber). In this case, autonomous systems may be employed to respond to such emergencies and have to be able to deal with unforeseen physical conditions and uncertainties, where the mechanical interaction with the environment is not only inevitable but also desirable to successfully perform their tasks. In this perspective, I discuss applications of soft robots for the protection of infrastructures, including recent advances in pipelines inspection, rubble search and rescue, and soft aerial manipulation, and promising perspectives on operations in radioactive environments, underwater monitoring and space exploration

Morphological Control of Cilia-Inspired Asymmetric Movements Using Nonlinear Soft Inflatable Actuators.

Soft robotic systems typically follow conventional control schemes, where actuators are supplied with dedicated inputs that are regulated through software. However, in recent years an alternative trend is being explored, where the control architecture can be simplified by harnessing the passive mechanical characteristics of the soft robotic system. This approach is named "morphological control", and it can be used to decrease the number of components (tubing, valves and regulators) required by the controller. In this paper, we demonstrate morphological control of bio-inspired asymmetric motions for systems of soft bending actuators that are interconnected with passive flow restrictors. We introduce bending actuators consisting out of a cylindrical latex balloon in a flexible PVC shell. By tuning the radii of the tube and the shell, we obtain a nonlinear relation between internal pressure and volume in the actuator with a peak and valley in pressure. Because of the nonlinear characteristics of the actuators, they can be assembled in a system with a single pressure input where they bend in a discrete, preprogrammed sequence. We design and analyze two such systems inspired by the asymmetric movements of biological cilia. The first replicates the swept area of individual cilia, having a different forward and backward stroke, and the second generates a travelling wave across an array of cilia

Autonomous Reading of Gauges in Unstructured Environments

This paper introduces GAUREAD, an end-to-end computer vision system that is able to autonomously read analogic gauges with circular shapes and linear scales in unstructured environments. Existing gauge reading software still relies on some manual entry, like the gauge location and the gauge scale, or they are able to work just with a frontal view. On the contrary, GAUREAD comprises all the necessary steps to make the measurement unconstrained from previous information, including gauge detection from scene, perspective rectification and scale reconstruction. Our algorithm achieves a speed of 800 milliseconds per reading on the NVIDIA Jetson Nano 4 GB. Experimental tests show that GAUREAD can provide a measurement with an error within 3% for perspective angles below 20° and within 9% up to 50°. The system is foreseen to be implemented on mobile robotics to automatise not only safety routines, but also critical security operations

Enhancing the quality of gauge images captured in haze and smoke scenes through deep learning

Images captured in hazy and smoky environments suffer from reduced visibility, posing a challenge when monitoring infrastructures and hindering emergency services during critical situations. The proposed work investigates the use of deep learning models to enhance the automatic, machine-based readability of gauge in smoky environments, with accurate gauge data interpretation serving as a valuable tool for first responders. The study utilizes two deep learning architectures, FFA-Net and AECR-Net, to improve the visibility of gauge images, corrupted with light up to dense haze and smoke. Since benchmark datasets of analog gauge images were unavailable, two synthetic datasets are generated using the Unreal Engine: a synthetic haze (approx. 4800 images) and a synthetic smoke (approx. 9600 images). Two datasets and two deep-learning frameworks allow the investigation of four different models. The models are trained with an 80% train, 10% validation, and 10% test split for the haze and smoke dataset, respectively. As a result, more robust results are retrieved from the AECR-Net, when compared to FFA-Net and to a prior-based model. For instance, for the synthetic haze dataset, the SSIM and PSNR metrics are about 0.98 and 43 dB obtained with AECR-Net, respectively, comparing well to state-of-the art results. The results from the synthetic smoke dataset are poorer, however the trained models still achieve interesting results. In general, imaging in the presence of smoke are more difficult to enhance given the inhomogeneity and high density. Secondly, FFA-Net and AECR-Net are implemented to dehaze and not to desmoke images. This work shows that use of deep learning architectures can improve the quality of analog gauge images captured in smoke and haze scenes immensely. Finally, the enhanced output images can be successfully post-processed for automatic autonomous reading of gauges

Towards enduring autonomous robots via embodied energy.

Autonomous robots comprise actuation, energy, sensory and control systems built from materials and structures that are not necessarily designed and integrated for multifunctionality. Yet, animals and other organisms that robots strive to emulate contain highly sophisticated and interconnected systems at all organizational levels, which allow multiple functions to be performed simultaneously. Herein, we examine how system integration and multifunctionality in nature inspires a new paradigm for autonomous robots that we call Embodied Energy. Whereas most untethered robots use batteries to store energy and power their operation, recent advancements in energy-storage techniques enable chemical or electrical energy sources to be embodied directly within the structures and materials used to create robots, rather than requiring separate battery packs. This perspective highlights emerging examples of Embodied Energy in the context of developing autonomous robots

How future surgery will benefit from SARS-COV-2-related measures: a SPIGC survey conveying the perspective of Italian surgeons

COVID-19 negatively affected surgical activity, but the potential benefits resulting from adopted measures remain unclear. The aim of this study was to evaluate the change in surgical activity and potential benefit from COVID-19 measures in perspective of Italian surgeons on behalf of SPIGC. A nationwide online survey on surgical practice before, during, and after COVID-19 pandemic was conducted in March-April 2022 (NCT:05323851). Effects of COVID-19 hospital-related measures on surgical patients' management and personal professional development across surgical specialties were explored. Data on demographics, pre-operative/peri-operative/post-operative management, and professional development were collected. Outcomes were matched with the corresponding volume. Four hundred and seventy-three respondents were included in final analysis across 14 surgical specialties. Since SARS-CoV-2 pandemic, application of telematic consultations (4.1% vs. 21.6%; p < 0.0001) and diagnostic evaluations (16.4% vs. 42.2%; p < 0.0001) increased. Elective surgical activities significantly reduced and surgeons opted more frequently for conservative management with a possible indication for elective (26.3% vs. 35.7%; p < 0.0001) or urgent (20.4% vs. 38.5%; p < 0.0001) surgery. All new COVID-related measures are perceived to be maintained in the future. Surgeons' personal education online increased from 12.6% (pre-COVID) to 86.6% (post-COVID; p < 0.0001). Online educational activities are considered a beneficial effect from COVID pandemic (56.4%). COVID-19 had a great impact on surgical specialties, with significant reduction of operation volume. However, some forced changes turned out to be benefits. Isolation measures pushed the use of telemedicine and telemetric devices for outpatient practice and favored communication for educational purposes and surgeon-patient/family communication. From the Italian surgeons' perspective, COVID-related measures will continue to influence future surgical clinical practice

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Biomimetic Ciliary Propulsion: Soft Robotic Actuation and Morphological Control

This research studies a biomimetic ciliary propulsion system, based on an array of independent pneumatically-actuated soft artificial cilia. The design of the system takes into account the characteristic nonreciprocal motions of biological cilia such as the spatial asymmetry of the single cilium motion and the metachronal wave effect of the whole cilia array. These motions are needed in order to generate a net fluid flow at low Reynolds number, which is the case of microfluidics applications and viscous fluidic propulsion. Net flows, measured with Particle Image Velocimetry (PIV) technique, is characterized in function of the nonreciprocal motions. Further, new fabrication processes for soft pneumatic actuators are investigated, including subtractive manufacturing for actuators with complex deformations and lithographic microfabrication for sub-mm actuators. Lastly, a new approach named 'morphological control' is developed to drive multiple artificial cilia using a single input, simplifying the control system.status: publishe

Design of a bi-segmented soft actuator with hardware encoded quasi-static inflation sequence

© 2018 IEEE. A soft actuator composed of two fluidic bending segments is designed and manufactured having a mechanically programmed sequence of inflation. The sequence is determined by analyzing the equilibrium states between the connected segments at each step of the inflation. To do this, segments are described using their inner pressure vs. volume expansion relationship. Since it may be difficult to formulate this expression analytically for complex soft inflatable structures, an approach based on nonlinear FEM simulations is here introduced: By modelling the inner cavity of an actuator as filled with incompressible fluid and generating a fluid flux during the simulation, the pressure-volume curve is easily obtained, even if highly nonlinear. The resulting nonlinearities are instrumental in generating inflation sequencing of multiple segmented actuators, which is an example of hardware-based intelligence. Exploiting these behaviours will greatly enhance the possibilities and performances of soft robots.status: publishe

Fabrication of High-Aspect-Ratio Cylindrical Micro-Structures Based on Electroactive Ionogel/Gold Nanocomposite

We present a fabrication process to realize 3D high-aspect-ratio cylindrical micro-structures of soft ionogel/gold nanocomposites by combining replica molding and Supersonic Cluster Beam Deposition (SCBD). Cylinders’ metallic masters (0.5 mm in diameter) are used to fabricate polydimethylsiloxane (PDMS) molds, where the ionogel is casted and UV cured. The replicated ionogel cylinders (aspect ratio > 20) are subsequently metallized through SCBD to integrate nanostructured gold electrodes (150 nm thick) into the polymer. Nanocomposite thin films are characterized in terms of electrochemical properties, exhibiting large double layer capacitance (24 μF/cm2) and suitable ionic conductivity (0.05 mS/cm) for charge transport across the network. Preliminary actuation tests show that the nanocomposite is able to respond to low intensity electric fields (applied voltage from 2.5 V to 5 V), with potential applications for the development of artificial smart micro-structures with motility behavior inspired by that of natural ciliate systems