Closed-Loop Control and Disturbance Mitigation of an Underwater Multi-Segment Continuum Manipulator

The use of soft and compliant manipulators in marine environments represents a promising paradigm shift for subsea inspection, with devices better suited to tasks owing to their ability to safely conform to items during contact. However, limitations driven by material characteristics often restrict the reach of such devices, with the complexity of obtaining state estimations making control non-trivial. Here, a detailed analysis of a 1m long compliant manipulator prototype for subsea inspection tasks is presented, including its mechanical design, state estimation technique, closed-loop control strategies, and experimental performance evaluation in underwater conditions. Results indicate that both the configuration-space and task-space controllers implemented are capable of positioning the end effector to desired locations, with deviations of <5% of the manipulator length spatially and to within 5^{o} of the desired configuration angles. The manipulator was also tested when subjected to various disturbances, such as loads of up to 300g and random point disturbances, and was proven to be able to limit displacement and restore the desired configuration. This work is a significant step towards the implementation of compliant manipulators in real-world subsea environments, proving their potential as an alternative to classical rigid-link designs.

Exploring Quantum Neural Networks for Demand Forecasting

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.

A Modular, Tendon Driven Variable Stiffness Manipulator with Internal Routing for Improved Stability and Increased Payload Capacity

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.