Autonomous Systems for Industrial and Marine Infrastructure Inspection and Protection

This Thesis focuses on the strategic engineering and application of AI-driven modeling and simulation of autonomous systems for the inspection and protection of critical marine and industrial infrastructures. The study includes multiple autonomous technologies including unmanned aerial vehicles (UAVs), unmanned underwater vehicles (UUVs), and surface vessels such as SWATH USVs to enhance inspection efficiency, reduce human risk, and provide real-time, high-quality inspection and survey. A key feature is the development of intelligent agents capable of autonomous decision-making, dynamic adaptation, and coordination across multi-domains: air, surface, underwater, seabed, space, and cyberspace in all scenarios. The research studies also the utilization of extended reality (XR) technologies to boost immersivity and interactivity of the systems in order to improve understanding of complex multidomain scenarios. Indeed, in addition to the above-mentioned traditional and innovative physical assets, the study addresses their projections and interactions in the cyberspace, which is essential to analyze vulnerabilities such as communication disruptions, system interferences and even swarm attacks. In fact, deliberate and accidental disruptive actions becoming more frequent which makes this study particularly relevant in the modern context. In this setting, a proposed applied scenario demonstrates the coordinated deployment of fleets of multidomain autonomous systems for offshore inspection, maintenance, monitoring, and protection, showcasing complex multi-robot operations under challenging conditions. This work contributes to the advancement of autonomous, AI-enhanced solutions for the resilient management and protection of critical infrastructures, emphasizing adaptive, multi-domain strategic engineering principles.