Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned underwater vehicles (UUV) development, since control system reliability strongly depends on the accuracy of attitude estimation. In small UUVs, typical redundant architectures, based on triplex redundancy, can represent a strong limitation in terms of payload and power consumption. This work proposes an FDI algorithm for small UUVs equipped with duplex sensor architecture. Here, attitude estimation relies upon the usage of two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The proposed SFDI algorithm is based on an unscented Kalman filter (UKF) approach to efficiently detect and isolate faulted IMU sensors. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real underwater vehicle. A sensitivity analysis was carried out on the relevant algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodynamic feasibility of the trajectories is guaranteed through a series of set-based arguments, which involve the solution of semi-definite programming problems. Navigation in the graph is performed through a hybrid A* algorithm whose performance metrics are improved through a properly trained classificator, which can forecast whether a candidate trajectory segment is feasible or not. The proposed solution is validated through numerical simulations, with a focus on the effects of different classificators features and by using two different kinds of artificial intelligence: a support vector machine (SVM) and a long-short term memory (LSTM). Results show up to a 28% reduction in computational effort and the importance of lowering the false negative rate in classification for achieving good planning performance outcomes.

We propose a detailed nonlinear mathematical model for a kite-like underwater energy generator describing its motion with six degrees of freedom (DoF), i.e. its translational and rotational coordinates and velocities in the presence of ocean currents. This work is the result of the long-term deployment of such a generator, the GEMSTAR prototype, which is operated in the laboratory facilities of RENEW-MEL. Marine renewables have attracted the attention of both the scientific community and industry due to their enormous production potential. Although several technologies are already available for commercial use, energy production from tidal currents still struggles with high operating costs, mainly due to component failures in the marine environment and the difficulty of maintenance underwater or at sea. To reduce these costs, it is necessary to carry out preventive maintenance and provide the equipment with fault detection and isolation functions. In view of the special characteristics of the GEMSTAR, it is also necessary to control its position to prevent the cable from twisting and to maximize its energy production by ensuring that the device is aligned with the current flow. For such tasks, the use of a digital twin is of great benefit. A digital twin is the digital counterpart to a physical device. It emulates a physical system with the help of an adaptable mathematical real-time model. In this work, the mathematical modeling of the device is performed by considering a few hypotheses: The mooring cable is assumed to be non-extensible and with a negligible weight. Furthermore, the hydrodynamic forces are modeled by considering the kite as a series of discrete bodies that do not interfere with each other's motion in the water. The dynamics of the kite with its six degrees of freedom are described by the description of the forces and moments acting on it, using a Newtonian approach for each of its components - the two turbines, the two tanks and the rudders. The model was developed in the MATLAB/Simulink environment and all its parameters can be easily modified to describe similar kite-like generators. The parameters for the model are obtained either from the design of the device or through numerical simulations that use Blade Moment Elementum Theory to model the behavior of the turbines. The validation of the model is based on real flow data from a future installation site, collected with an acoustic Doppler current profiler (ADCP) over one-year measurement campaigns. The performance of the model will be further evaluated with a physical prototype that will be tested at sea as part of the RENEW-MEL laboratory experiments. The study also demonstrates the model’s ability to operate in real time, a prerequisite for digital twin applications. Once the GEMSTAR prototype has been deployed at sea, its digital twin will be experimentally validated and used for maintenance and control of the device. This will allow fine-tuning of the model parameters and testing its ability to predict the operation and lifetime of GEMSTAR, with particular attention to the effects of biofouling phenomena.