Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research. WiMUST uses a team of cooperative autonomous ma- rine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicat- ing nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely au- tomatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring inter- vehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.Peer reviewe

Controllo basato su visione per velivoli multirotore

In questo lavoro di tesi è stato sviluppato un sistema di stima della posa e di controllo di un multirotore basato sulla visione. L'approccio proposto utilizza dati provenienti da sensori inerziali, sensori ad ultrasuoni e particolari Marker ottici. L'utilizza del filtro di Kalman ha reso possibile la sensor fusion fra dati inerziali e visione.Sono stati poi implementati sia controllori lineari PID per il controllo di assetto e posizione sia un un controllo di posizione non lineare. E' stato infine realizzato un velivolo prototipale, dotato di sistema operativo real time, per l'esecuzione delle test sperimentali. Gli esperimenti hanno mostrato come la fusione fra visione e dati inerziali abbia fornito un ottima accuratezza di stima, con robustezza in caso di perdita del feedback visivo per breve tempo. Il lavoro continuerà nel miglioramento della stima di posizione, con l'obbiettivo di integrare il sistema in un contesto di localizzazione mista

Advanced Control and Optimization for Hybrid ROV/AUV Vehicles

Questo lavoro affronta alcuni temi di diversa natura ma in egual modo rilevanti nel mondo dei veicoli sottomarini e si focalizza sullo studio di tecniche utili alla sfera dei veicoli ibridi ROV/AUV i quali uniscono le caratteristiche funzionali dei veicoli a pilotaggio remoto con la flessibilità dei veicoli subacquei autonomi. Questa tipologia di veicoli richiede un sistema di controllo ad alta precisione durante le fasi di intervento ed un elevato risparmio energetico durante le fasi di navigazione a lungo raggio. La tesi è organizzata in tre parti che separano gli argomenti in esame: "Modeling", "Position Control and Guidance" and "Towards Minimum Consumption". La prima parte descrive la modellazione matematica generale della dinamica dei veicoli subacquei e il modello matematico dei sistemi di propulsione. La seconda parte riguarda algoritmi di controllo e di guida idonei per veicoli ibridi ROV/AUV a partire dal problema del controllo di posizione e di assetto in presenza di disturbi esterni. Questo primo problema è stato affrontato considerando una variante dello Sliding Mode Control (SMC) conosciuta come Super Twisting Algorithm (STA) la quale è stata utilizzata per il posizionamento robusto del veicolo. L'approccio è stato validato in simulazione e confrontato con un classico approccio basato su controllori PID. A seguire, nella seconda parte si mostra anche come Sistemi di Guida Fuzzy (FGS) possano essere utilizzati per il rendezvous e l'inseguimento di bersagli statici, mobili ed acceleranti. Combinazioni di Sistemi Fuzzy Takagi-Sugeno sono usati per definire campi vettoriali per guidare il veicolo inseguitore verso il bersaglio garantendo traiettorie morbide. La terza sezione della tesi tratta gli aspetti relativi al risparmio energetico nei veicoli ROV/AUV durante la navigazione di crociera. Viene mostrato come l'interazione tra i propulsori possa influire sull'efficienza energetica, ed è inoltre mostrato come un'ottimizzazione dell'allocazione dei propulsori possa implicare un reale risparmio energetico globale. A questo scopo è stato utilizzato un approccio di ottimizzazione in tempo reale noto come Extremum Seeking (ES) per cercare il minimo della potenza totale assorbita dal sistema di attuazione. A seguire, l'approccio basato su Extremum Seeking è stato utilizzato anche, sul modello planare di un ROV/AUV, per trovare un offset sul riferimento di assetto che garantisse il minimo consumo energetico senza alcuna conoscenza del modello idrodinamico del veicolo e degli effetti idrodinamici sui propulsori. This work is inspired by the domain of underwater vehicles and covers various topics that are all equally important. The subjects covered include attractive topics within the emerging world of Hybrid ROV/AUV vehicles, which combine the functionality of Remotely Operated Vehicles with the flexibility of Autonomous Underwater Vehicles. The Hybrid ROV/AUV vehicles require high precision control during the intervention phases and high energy saving capabilities during the long-range monitoring mission. The thesis is organized in three parts that separate the subjects under analysis: "Modeling", "Position Control and Guidance" and "Towards Minimum Consumption". The first part describes the general mathematical modeling of underwater vehicle dynamics and propulsion systems. The second part of the work deals with control and guidance algorithms suitable for Intervention ROV/AUV, starting from the vehicle's position and attitude control in the presence of external disturbances. This control problem is addressed with a variant of the Sliding Mode Control (SMC) known as Super Twisting Algorithm (STA) that is used for robust positioning of the vehicle in the presence of external disturbances; the results were compared to a classical PID technique using simulations. The second part also describes how Fuzzy Guidance Systems (FGS) can be used for rendezvous and pursuit of static and moving targets. An FGS is used to define vector fields to guide the pursuer toward the target by smooth trajectories. The third part of the thesis deals with energy-saving aspects, focusing on energy saving during the cruising mode. The thruster-thruster interaction phenomenon is first discussed, as well as how it may affect underwater vehicle energy efficiency. An Extremum Seeking (ES) real-time optimizations approach is proposed to optimize the thruster allocation process to reduce the total absorbed power without knowing the interaction model. Finally, the ES algorithm is then used to adapt the heading set-point of an underwater vehicle to reach the minimum energy consumption as a function of hydrodynamic effects

Super Twisting Sliding Mode Control for Precise Control of Intervention Autonomous Underwater Vehicles

In this work we address the problem of precise positioning and trajectory tracking for an Intervention Autonomous Underwater Vehicle (I-AUV). Two solutions, based on the Variable Structure Systems (VSS) theory, are proposed with focus on the Super Twisting Sliding Mode methodology. We employ two different sliding mode approaches in comparison with a commonly used double loop architecture PID approach. The tuning of the controllers was carried out starting from the knowledge of the I-AUV real parameters. The performance of the proposed algorithms was verified via simulations in presence of external disturbances on a 6-DoF industrial simulator provided by Saipem

A Fuzzy Guidance System for Rendezvous and Pursuit of Moving Targets

This article presents the development of a fuzzy guidance system (FGS) for unmanned aerial vehicles capable of pursuing and performing rendezvous with static and mobile targets. The system is designed to allow the vehicle to approach a maneuvering target from a desired direction of arrival and to terminate the rendezvous at a constant distance from the target. In order to perform a rendezvous with a maneuvering target, the desired direction of arrival is adjusted over time to always approach the target from behind, so that the aircraft and target velocity vectors become aligned. The proposed guidance system assumes the presence of an autopilot and uses a set of Takagi–Sugeno fuzzy controllers to generate the orientation and speed references for the velocity and heading control loops, given the relative position and velocity between the aircraft and the target. The FGS treats the target as a mobile waypoint in a 4-D space (position in 2-dimensions, desired crossing heading and speed) and guides the aircraft on suitable trajectories towards the target. Only when the vehicle is close enough to the rendezvous point, the guidance law is complemented with an additional linear controller to manage the terminal formation keeping phase. The capabilities of the proposed rendezvous-FGS are verified in simulation on both maneuvering and non-maneuvering targets. Finally, experimental results using a multi-rotor aerial system are presented for both fixed and accelerating targets

Multispectral Satellite Image Generation Using StyleGAN3

Satellite-based remote sensing images are essential for Earth surface analysis, serving diverse purposes in both civilian and military domains. Satellite images are used for analysis and decision making and are considered a reliable source of information. Recently, the field of image generation has developed increasingly sophisticated techniques, such as generative neural models, usually known as generative adversarial networks (GANs), to create synthetic images from scratch that appear almost real. Generative models have traditionally been applied to RGB or grayscale images and have been used for generating fake images of faces, animals, or objects. Currently, there are few studies regarding the application of GAN to multispectral satellite images. This work aims to test GAN models against the generation of multispectral satellite images, and in particular, the work explores the ability of the state-of-the-art StyleGAN3 model to produce high-quality synthetic Sentinel-2 images. The work delves into the configuration, training process, and evaluation of StyleGAN3 using custom Sentinel-2 datasets. StyleGAN3 results are compared with those provided by the proGAN model, the only GAN model tested so far on multispectral satellite data. Evaluation methods include visual inspection, spectral signature analysis, and a modified Fréchet inception distance for quantitative assessment. Results show that StyleGAN3 outperforms proGAN model exhibiting visually pleasing images. The quantitative comparison shows that StyleGAN3 provides the best results in terms of FID scores, in particular the improvement compared to proGAN increases as the spatial extent and spectral dimension of the generated images increases