Dr. Sergio Domínguez Profile

Dr. Sergio Domínguez

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Proceedings Article | 13 November 2024 Poster + Paper

Comparative energy management system study for efficient and agile hybrid unmanned wing-in-ground vehicles

Riccardo Pasquino, Sofija Ilic, Sarthak Mishra, Zorana Milosevic, Sergio Domínguez

Proceedings Volume 13207, 132070K (2024) https://doi.org/10.1117/12.3031730

KEYWORDS: Batteries, Autonomous vehicles, Energy efficiency, Data modeling, Fuzzy logic, Control systems, Design, Defense and security, Transportation, Hydrogen

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The transition towards more-electric and autonomous vehicles in defence and maritime applications necessitates innovative approaches to energy management, particularly for hybrid systems combining fuel cells, batteries, and supercapacitors. This paper explores the development of an Energy Management System (EMS) tailored for Hybrid Unmanned Wing-In-Ground (WIG) Vehicles, leveraging comparative analyses from the more-electric aircraft sector to enhance reliability and operational flexibility while supporting the rapid manoeuvres crucial to defence applications.

Hybrid WIG vehicles, operating at the intersection of air and sea, present unique challenges and opportunities for energy optimisation. Drawing upon established energy management strategies—including state machine control, rule-based fuzzy logic, classical PI control, frequency decoupling/state machine, the equivalent consumption minimisation strategy, the external energy maximisation strategy and a new proposed control strategy modified version of the classical PI—this study adapts these methodologies to the specific requirements of unmanned maritime vehicles. Our work optimises hydrogen consumption, manages the state of charge for batteries and supercapacitors, and enhances overall system efficiency while considering the rapid manoeuvrability essential for defence missions.

This research identifies the most effective EMS approaches in sustaining high-performance and environmentally conscious operations while adequately supporting the high-stakes, fast-paced manoeuvres integral to defence strategies through simulation and experimental validation of a representative hybrid WIG vehicle model. The findings contribute to the advancement of autonomous naval technologies and offer insights into the broader application of hybrid energy systems in future more-electric vehicles.

Proceedings Article | 13 November 2024 Presentation + Paper

Obstacle avoidance for unmanned wing-in-ground vehicles

M. Domínguez, S. Mishra, S. Ilić, Z. Milošević, S. Domínguez

Proceedings Volume 13207, 1320708 (2024) https://doi.org/10.1117/12.3031708

KEYWORDS: Artificial intelligence, Convolutional neural networks, Image segmentation, Autonomous vehicles, Image resolution, Image classification, Defense and security, Video, Cameras, Reflection

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The use of Wing-In-Ground (WIG) vehicles marks a significant evolution in autonomous transportation, bridging the gap between aerial and maritime domains and combining maritime vessels' efficiency with aircraft speed and flexibility. These vehicles navigate the complex interface between sea and air, requiring sophisticated navigational strategies to manage their unique dynamics. Central to their deployment in defence and security applications is the ability to rapidly deploy and intervene at sea without infrastructure or launch vehicles for departure and landing. This paper presents an obstacle avoidance framework for Unmanned WIG Vehicles (UWVs) that integrates advanced image segmentation techniques, drawing upon comprehensives datasets for obstacle detection and avoidance.

The datasets chosen for training and testing encompass a wide range of maritime scenarios, including lakes, rivers, and seas, serve as the foundation for this study. It offers various scene types, obstacle classifications, and environmental conditions.

The study of different image segmentation CNNs represents a pivotal step towards robust autonomy in UWVs, particularly in defence and security, where reliability and precision are paramount. The methodology presented may establish the foundation for an obstacle avoidance system that improves the operational efficiency of UWVs while enhancing their safety and providing a more accurate and collision-free navigation through the dynamically changing maritime environments.

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