Variable Stiffness Mechanisms (VSM) are becoming ubiquitous in mechatronics given the benefit they provide in terms of safety and performance. Despite these assets, VSMs remain fairly complex mechanical devices lacking in compactness, ease of manufacturing and accessibility. In addition, the scarcity of commercially available VSMs requires that such systems are mostly designed in-house. We propose a new type of VSM that improves on the pre-existing Jack Spring concept by making it more compact and robust. The new concept, which we refer to as the Compact Modifier of Active Coils (C-MAC) mechanism, is specifically designed to be manufactured through a monolithic 3D print. This approach enables to modify a minimal set of design features, namely the spring diameter and the coil diameter, to achieve the desired range of stiffness variation. We test the proposed design on six configurations; these show hysteretic energy losses no larger than 35% over the stiffness variation and confirm stiffness to scale according to theory. Stiffness ranging from 0.15 N/mm to 1.02N/mm were measured for an overall device length of 140 mm, including a maximal stroke length of 22 mm. The results confirm excellent scalability and manufacturability of the proposed design, providing a versatile mechanism for fast prototyping and the development of entire 3D printed robotic systems embedding variable stiffness capabilities.

Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two drawbacks which limit the scale of continuum designs, preventing widespread adoption of this technology. To tackle these problems, this work details the design and experimental testing of a modular, tendon driven bead-style continuum manipulator with tunable stiffness. By embedding the ability to independently control the stiffness of distinct sections of the structure, the manipulator can regulate it’s posture under greater loads of up to 1kg at the end-effector, with reference to the flexible state. Likewise, an internal routing scheme vastly improves the stability of the proximal segment when operating the distal segment, reducing deviations by at least 70.11%. Operation is validated when gravity is both tangential and perpendicular to the manipulator backbone, a feature uncommon in previous designs. The findings presented in this work are key to the development of larger scale continuum designs, demonstrating that flexibility and tip stability under loading can co-exist without compromise.

Justifying operational decisions for robots is a challenging task as the operator or the robot itself has to understand the underlying physical interaction between the robot and the environment to predict the potential outcome. It is desirable to understand how the decision influences the operational performance in the way of causal relationship for the purpose of explainable decision-making. Here we propose a novel causal inference framework for the discovery and inference on the reasoning of the operational decisions for robots. It unifies both domain knowledge integration and model-free causal inference, allowing a data-driven causal knowledge learning on time series data. The framework is evaluated in the experiments of an underwater robot with complex environmental interactions. The results show that the framework can learn the causal structure and inference model to accurately explain and predict the operation performance with integrated physics.

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.

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.