Operations in extreme and hostile environments, such as offshore oil and gas production, nuclear decommissioning, nuclear facility maintenance, deep mining, space exploration, and subsea applications, require the execution of sophisticated tasks. In nuclear environments, robotic systems have advanced significantly over the past years but still suffer from task failures caused by informational and physical uncertainty of the highly unstructured nature of the environment and exasperated by the time constraints imposed by high radiation levels. Herein, a survey is presented of current robotic systems that can operate in such extreme environments and offer a novel approach to solving the challenges they impose, encapsulated by the mission statement of providing structure in unstructured environments and exemplified by a new self‐assembling modular robotic system, the Connect‐R.

Developing soft circuits from individual soft logic gates poses a unique challenge: with increasing numbers of logic gates, the design and implementation of circuits lead to inefficiencies due to mathematically unoptimized circuits and wiring mistakes during assembly. It is therefore practically important to introduce design tools that support the development of soft circuits. We developed a web-based graphical user interface, the Soft Compiler, that accepts a user-defined robot behavior as a truth table to generate a mathematically optimized circuit diagram that guides the assembly of a soft fluidic circuit. We describe the design and experimental verification of three soft circuits of increasing complexity, using the Soft Compiler as a design tool and a novel pneumatic glove as an input interface. In one example, we reduce the size of a soft circuit from the original 11 logic gates to 4 logic gates while maintaining circuit functionality. The Soft Compiler is a web-based design tool for fluidic, soft circuits and published under an open-source MIT License.

The ability to navigate complex unstructured environments and carry out inspection tasks requires robots to be capable of climbing inclined surfaces and to be equipped with a sensor payload. These features are desirable for robots that are used to inspect and monitor offshore energy platforms. Existing climbing robots mostly use rigid actuators, and robots that use soft actuators are not fully untethered yet. Another major problem with current climbing robots is that they are not built in a modular fashion, which makes it harder to adapt the system to new tasks, to repair the system, and to replace and reconfigure modules. This work presents a 450 g and a 250 × 250 × 140 mm modular, untethered hybrid hard/soft robot—Limpet II. The Limpet II uses a hybrid electromagnetic module as its core module to allow adhesion and locomotion capabilities. The adhesion capability is based on negative pressure adhesion utilizing suction cups. The locomotion capability is based on slip-stick locomotion. The Limpet II also has a sensor payload with nine different sensing modalities, which can be used to inspect and monitor offshore structures and the conditions surrounding them. Since the Limpet II is designed as a modular system, the modules can be reconfigured to achieve multiple tasks. To demonstrate its potential for inspection of offshore platforms, we show that the Limpet II is capable of responding to different sensory inputs, repositioning itself within its environment, adhering to structures made of different materials, and climbing inclined surfaces.

The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshore energy sector. Data, in under-sampled regions of the ocean and in the offshore patches where the industrial expansion is taking place, are fundamental to manage successfully a sustainable development and to mitigate climate change. Existing technology cannot cope with the vast and harsh environments that need monitoring and sampling the most. The limiting factors are, among others, the spatial scales of the physical domain, the high pressure, and the strong hydrodynamic perturbations, which require vehicles with a combination of persistent autonomy, augmented efficiency, extreme robustness, and advanced control. In light of the most recent developments in soft robotics technologies, we propose that the use of soft robots may aid in addressing the challenges posed by abyssal and wave-dominated environments. Nevertheless, soft robots also allow for fast and low-cost manufacturing, presenting a new potential problem: marine pollution from ubiquitous soft sampling devices. In this study, the technological and scientific gaps are widely discussed, as they represent the driving factors for the development of soft robotics. Offshore industry supports increasing energy demand and the employment of robots on marine assets is growing. Such expansion needs to be sustained by the knowledge of the oceanic environment, where large remote areas are yet to be explored and adequately sampled. We offer our perspective on the development of sustainable soft systems, indicating the characteristics of the existing soft robots that promote underwater maneuverability, locomotion, and sampling. This perspective encourages an interdisciplinary approach to the design of aquatic soft robots and invites a discussion about the industrial and oceanographic needs that call for their application.

Deployment of the Internet of Things (IoT) in real industrial scenarios is fraught with unseen challenges and planning intricacies. The inside engineering and methodologies behind a successful Industrial IoT (IIoT) setup are seldom discussed. In this paper, we describe a systems approach to identify, integrate, and deploy the different IIoT modules in a bottom-up manner. The process that go from first identifying and labelling the various assets to be monitored, to their abstraction and integration for a given industrial scenario, forms the major crux of the work presented in this paper. We deployed several sensor nodes in the Offshore Renewable Energy Catapult site, located in Blyth, UK. Each sensor node monitored a specific asset and communicated via LoRaWAN to our local Data Hub. A simple query interface and visualisation dashboard allowed real-time data assessment and rapid asset monitoring. To make the proposed approach easy to comprehend and applicable to other scenarios, we extracted a minimal translational bottom-up flowchart. The systems approach described here offers a robust methodology to plan out a real-world IIoT deployment.

Soft pneumatic actuators are very popular in the soft robotic community due to their ease of manufacturing and simplicity of control. Currently, the efficiency of such soft actuators and their ability to do useful work are rarely investigated in a formal approach. The lack of task-orientated development approaches presents a barrier to utilize soft robotic systems in our everyday lives. In this paper, we describe an experimental approach based on port-Hamiltonian theory applied on a type of pneumatic network (pneu-net) actuator to investigate the efficiency of task-orientated work. We can obtain efficiency from the external interactions of the port-Hamiltonian system. If we can minimize the internal energy interactions, then the power continuous nature of the port-Hamiltonian structure ensures more input energy will result in more useful work done at the output. We found out that higher efficiency actuators can be achieved with a softer material and a thinner wall thickness in the desired direction of the deformation. The internal mechanical energy storage is reduced as a result. However, if the task requires a higher work-done then a stiffer material is required. We can start to define a design approach based on the task. The task can be generalized in terms of energy. We can select the material properties suitable for the magnitude of work done. We can design the geometry to minimize the internal energy stored. The empirical model of the port-Hamiltonian structure provides insights into how the mechanical efficiency varies in terms of design parameters and the port-Hamiltonian approach is a step towards more practical, task-orientated soft robotic systems.

The ignition of flammable liquids and gases in offshore oil and gas environments is a major risk and can cause loss of life, serious injury, and significant damage to infrastructure. Power supplies that are used to provide regulated voltages to drive motors, relays, and power electronic controls can produce heat and cause sparks. As a result, the European Union requires ATEX certification on electrical equipment to ensure safety in such extreme environments. Implementing designs that meet this standard is time-consuming and adds to the cost of operations. Soft robots are often made with soft materials and can be actuated pneumatically, without electronics, making these systems inherently compliant with this directive. In this paper, we aim to increase the capability of new soft robotic systems moving from a one-to-one control-actuator architecture and implementing an electronics-free control system. We have developed a robot that demonstrates locomotion and gripping using three-pneumatic lines: a vacuum power line, a control input, and a clock line. We have followed the design principles of digital electronics and demonstrated an integrated fluidic circuit with eleven, fully integrated fluidic switches and six actuators. We have realized the basic building blocks of logical operation into combinational logic and memory using our fluidic switches to create a two-state automata machine. This system expands on the state of the art increasing the complexity over existing soft systems with integrated control.

Peristaltic conveyance can be used for the sorting and transport of delicate and nonrigid objects such as meat or soft fruit. The non‐linearity and stochastic behavior of peristaltic systems make them difficult to control. Optimizing controllers using machine learning represents a promising path to effective peristaltic control but currently, there is no suitable simulated model of a peristaltic table in which to run these optimizations. A simple, simulated model of a peristaltic conveyor that can be used for optimizing peristaltic control on a variety of peristaltic tables is presented. This simulator is demonstrated through a limited control problem evaluated on our real‐world system that is built for peristaltic conveyance. This simulator is available as the python package PeriSim so that it can be used by the robotics community for peristaltic control development.

Soft robots are a new class of systems being developed and studied by robotics scientists. These systems have a diverse range of applications including sub-sea manipulation and rehabilitative robotics. In their current state of development, the prevalent paradigm for the control architecture in these systems is a one-to-one mapping of controller outputs to actuators. In this work, we define functional blocks as the physical implementation of some discrete behaviors, which are presented as a decomposition of the behavior of the soft robot. We also use the term 'stacking' as the ability to combine functional blocks to create a system that is more complex and has greater capability than the sum of its parts. By stacking functional blocks a system designer can increase the range of behaviors and the overall capability of the system. As the community continues to increase the capabilities of soft systems-by stacking more and more functional blocks we will encounter a practical limit with the number of parallelized control lines. In this paper, we review 20 soft systems reported in the literature and we observe this trend of one-to-one mapping of control outputs to functional blocks. We also observe that stacking functional blocks results in systems that are increasingly capable of a diverse range of complex motions and behaviors, leading ultimately to systems that are capable of performing useful tasks. The design heuristic that we observe is one of increased capability by stacking simple units-a classic engineering approach. As we move towards more capability in soft robotic systems, and begin to reach practical limits in control, we predict that we will require increased amounts of autonomy in the system. The field of soft robotics is in its infancy, and as we move towards realizing the potential of this technology, we will need to develop design tools and control paradigms that allow us to handle the complexity in these stacked, non-linear systems.

In this paper, we present the results of deploying the first test prototype of the USMART low cost underwater sensor network in sea trials in Fort William, United Kingdom, on 29 June 2018 and 3 July 2018. We demonstrate the first ever hardware implementation of the Transmit Delay Allocation MAC (TDA-MAC) protocol for data gathering in underwater acoustic sensor networks (UASNs). The results show a successful application of TDA-MAC to remote environmental monitoring, integrating a range of sensor nodes developed by the universities of Heriot-Watt, York, Newcastle and Edinburgh. We focus on the practical challenges and their mitigation strategies related to TDA-MAC to increase its robustness in real-world deployments, compared with theoretical and simulation-based studies. The lessons learned from the sea trials reported in this paper prompted several crucial modifications to TDA-MAC which form a solid foundation for further work on the development of TDA-MAC based UASNs.

Robots performing automated tasks in uncontrolled environments need to adapt to environmental changes. Through building large collectives of robots, this robust and adaptive behavior can emerge from simple individual rules. These collectives can also be reconfigured, allowing for adaption to new tasks. Larger collectives are more robust and more capable, but the size of existing collectives is limited by the cost of individual units. In this article, we present a soft, modular robot that we have explicitly designed for manufacturability: Linbots. Linbots use multifunctional voice coils to actuate linearly, to produce audio output, and to sense touch. When used in collectives, the Linbots can communicate with neighboring Linbots allowing for isolated behavior as well as the propagation of information throughout a collective. We demonstrate that these collectives of Linbots can perform complex tasks in a scalable distributed manner, and we show transport of objects by collective peristalsis and sorting of objects by a two-dimensional array of Linbots.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair, and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new multi-sensing platform, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (Underwater Autonomous Vehicles, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal-processing techniques. We show an example of this approach for monitoring offshore wind turbines, by designing an experimental setup to mimic a wind turbine using a stepper motor and custom-designed acrylic fan blades. We use the distance sensor, which is a Time-of-Flight sensor, to achieve the monitoring process. We use two different approaches for the condition monitoring process: offline and online classification. We tested the offline classification approach using two different communication techniques: serial and Wi-Fi. We performed the online classification approach using two different communication techniques: LoRa and optical. We train our classifier offline and transfer its parameters to the Limpet for online classification. We simulated and classified four different faults in the operation of wind turbines. We tailored a data processing procedure for the gathered data and trained the Limpet to distinguish among each of the functioning states. The results show successful classification using the online approach, where the processing and analysis of the data is done on-board by the microcontroller. By using online classification, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. This work shines light on how robots can perform on-board signal processing and analysis to gain multi-functional sensing capabilities, improve their communication requirements, and monitor the structural health of equipment.

Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. Instead of adding functionality by adding more components and therefore increasing the cost, we demonstrate how low-cost hardware can be used beyond its standard functionality. We systematically review 15 swarm robotic systems and analyse their sensing capabilities by applying a general sensor model from the sensing and measurement community. This work is based on the HoverBot system. A HoverBot is a levitating circuit board that manoeuvres by pulling itself towards magnetic anchors that are embedded into the robot arena. We show that HoverBot’s magnetic field readouts from its Hall-effect sensor can be associated to successful movement, robot rotation and collision measurands. We build a time series classifier based on these magnetic field readouts. We modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot’s low-cost microcontroller. We enabled HoverBot with successful movement, rotation, and collision sensing capabilities by utilising its single Hall-effect sensor. We discuss how our classification method could be applied to other sensors to increase a robot’s functionality while retaining its cost.

Scaling up robot swarms to collectives of hundreds or even thousands without sacrificing sensing, processing, and locomotion capabilities is a challenging problem. Low-cost robots are potentially scalable, but the majority of existing systems have limited capabilities, and these limitations substantially constrain the type of experiments that could be performed by robotics researchers. As an alternative to increasing the quantity of robots by reducing their functionality, we have developed a new technology that delivers increased functionality at low-cost. In this study, we present a comprehensive literature review on the most commonly used locomotion strategies of swarm robotic systems. We introduce a new type of low-friction locomotion-active low-friction locomotion-and we show its first implementation in the HoverBot system. The HoverBot system consists of an air levitation and magnet table, and a HoverBot agent. HoverBot agents are levitating circuit boards that we have equipped with an array of planar coils and a Hall-effect sensor. The HoverBot agent uses its coils to pull itself toward magnetic anchors that are embedded into a levitation table. These robots use active low-friction locomotion; consist of only surface-mount components; circumvent actuator calibration; are capable of odometry by using a single Hall-effect sensor; and perform precise movement. We conducted three hours of experimental evaluation of the HoverBot system in which we observed the system performing more than 10,000 steps. We also demonstrate formation movement, random collision, and straight collisions with two robots. This study demonstrates that active low-friction locomotion is an alternative to wheeled and slip-stick locomotion in the field of swarm robotics.

Objectives This study aimed to detect the relation between fasting C-peptide level and serum insulin level in pre-eclamptic pregnant women. Background Pre-eclampsia is a systemic disease that is characterized by increased vascular resistance, endothelial dysfunction, proteinuria, coagulopathy, and hypertension. Similarities in certain biochemical variables between pre-eclampsia and the insulin resistance syndrome imply a possible link between insulin resistance and pre-eclampsia. Patients and methods A total of 60 pregnant women were included in this study after the approval of the ethical committee of the Alexandria University (where the study was conducted in); then, they were divided into two groups: 30 normal pregnant women as controls and another 30 pregnant women with the complication of pre-eclampsia served the study group. This group was subdivided into 10 patients with severe pre-eclampsia and another 20 with mild pre-eclampsia. All the patients were subjected to the oral glucose tolerance test (OGTT), assessment of fasting serum insulin and fasting C-peptide, and urine for dipstick. Results A statistically significant difference was found between the two groups in the mean arterial blood pressure, systolic blood pressure, and diastolic blood pressure. There was a statistically significant difference between the two groups in BMI as increased BMI was associated with pre-eclampsia. There were statistically significant differences between the two groups in serum C-peptide, serum insulin, and protein in urine. Conclusion Serum C-peptide and serum insulin levels were significantly lower in women with severe pre-eclampsia than in women with mild pre-eclampsia and normotensive pregnant women, with weak associations to pre-eclampsia. BMI was significantly higher in women with severe and mild pre-eclampsia than in normotensive pregnant women, with strong associations to pre-eclampsia.

Objective The aim of the study was to assess the predictive value of interleukin 28B (IL-28B) rs12979860 polymorphism in the response to interferon (IFN)/ribavirin (RBV) therapy in hepatitis C virus (HCV) patients and the allele frequencies of this gene in HCV patients as compared with healthy controls. Background HCV infection, one of the major causes of liver disease worldwide, is highly prevalent in Egypt, with a predominance of HCV genotype 4. The standard of care for chronic HCV infection consists of treatment with pegylated interferon-a plus ribavirin (PegIFN/RBV), which is prolonged and costly and is associated with dose-limiting side effects, increasing the need for accurate prediction of treatment failure. IL-28B gene polymorphism has been shown to be related to HCV treatment response, mainly in genotype 1. Patients and methods This study included 130 HCV-positive Egyptian patients treated with IFN/RBV therapy. They were divided into three groups according to their response to therapy. Group I included 70 patients with sustained viral response (SVR). Group II included 49 patients with no response. Group III included 11 patients with relapse, and group IV included 40 healthy controls. Liver function tests, complete blood count, evaluation of viral markers, HCV-RNA by PCR, and evaluation for IL-28B single nucleotide polymorphisms for rs12979860 were performed by PCR-RFLP in all patients. Results IL-28B genotype CC was present in 34.6% of patients, whereas CT and TT genotypes were detected in 42.3 and 23.1% of patients, respectively. Seventeen (53.8%) patients achieved an SVR, whereas 60 (46.2%) did not. Of the 70 patients with an SVR, 38 had genotype CC, 25 had genotype CT, and seven had genotype TT. Patients having the CC genotype achieved significantly higher SVR rates (84.4%) compared with CT (45.5%) and TT patients (23.3%). Conclusion The IL-28B polymorphism is an independent predictor of SVR to PegIFN/RBV in Egyptian HCV-positive patients with genotype 4.