The Limpet: A ROS-Enabled Multi-Sensing Platform for the ORCA Hub.

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.

Soft Robotics: A Route to Equality, Diversity, and Inclusivity in Robotics.

Robotics is entering our daily lives. The discipline is increasingly crucial in fields such as agriculture, medicine, and rescue operations, impacting our food, health, and planet. At the same time, it is becoming evident that robotic research must embrace and reflect the diversity of human society to address these broad challenges effectively. In recent years, gender inclusivity has received increasing attention, but it still remains a distant goal. In addition, awareness is rising around other dimensions of diversity, including nationality, religion, and politics. Unfortunately, despite the efforts, empirical evidence shows that the field has still a long way to go before achieving a sufficient level of equality, diversity, and inclusion across these spectra. This study focuses on the soft robotics community-a growing and relatively recent subfield-and it outlines the present state of equality and diversity panorama in this discipline. The article argues that its high interdisciplinary and accessibility make it a particularly welcoming branch of robotics. We discuss the elements that make this subdiscipline an example for the broader robotic field. At the same time, we recognize that the field should still improve in several ways and become more inclusive and diverse. We propose concrete actions that we believe will contribute to achieving this goal, and provide metrics to monitor its evolution.

A framework for FAIR robotic datasets.

It is essential to publish and make available environmental data gathered by emerging robotic platforms to contribute to the Global Ocean Observing System (GOOS), supported by the United Nations - Decade of Ocean Science for Sustainable Development (2021-2030). The transparency of these unique observational datasets needs to be supported by the corresponding robotic records. The data describing the observational platform behaviour and its performance are necessary to validate the environmental data and repeat consistently the in-situ robotic deployment. The Free and Open Source Software (FOSS), proposed in this manuscript, describes how, using the established approach in Earth Sciences, the data characterising marine robotic missions can be formatted and shared following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The manuscript is a step-by-step guide to render marine robotic telemetry FAIR and publishable. State-of-the-art protocols for metadata and data formatting are proposed, applied and integrated automatically using Jupyter Notebooks to maximise visibility and ease of use. The method outlined here aims to be a first fundamental step towards FAIR interdisciplinary observational science.

Soft Robots for Ocean Exploration and Offshore Operations: A Perspective.

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.

Limpet II: A Modular, Untethered Soft Robot.

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.

Capability by Stacking: The Current Design Heuristic for Soft Robots.

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.