Mobile robots are increasingly deployed in hazardous or inaccessible environments to perform inspection, intervention, and data collection tasks. However, navigating such environments is far more complex than simple obstacle avoidance: robots must also deal with communication blackouts, contamination risks, limited onboard energy, and incomplete or evolving maps. A previous PhD project (2023–2026) introduced a multi-criteria navigation framework based on layered environmental mapping and weighted decision aggregation, demonstrating its feasibility in simulated, static scenarios.

The proposed thesis aims to extend this approach to dynamic and partially unknown environments, enabling real-time adaptive decision-making. The work will rely on tools from mobile robotics, data fusion, and autonomous planning, supported by experimental facilities that allow realistic validation. The objective is to bring navigation strategies closer to real operational conditions encountered in nuclear dismantling sites and other industrial environments where human intervention is risky. The doctoral candidate will benefit from an active research environment, multidisciplinary collaborations, and strong career opportunities in autonomous robotics and safety-critical intervention systems.