Résumé
Current robotic navigation methods primarily focus on obstacle detection and avoidance, often neglecting other crucial environmental criteria essential for mission-critical scenarios, such as defense, space exploration, naval operations, or nuclear environments. This paper introduces a structured, multi-criteria navigation methodology specifically designed for complex environments, illustrated through a simulated nuclear case study. The proposed approach integrates various sensor-derived environmental factors—including obstacles, network connectivity and radiation dose rates—into a unified, multi-layered spatial representation generated through sensor fusion techniques and localization systems (e.g., SLAM). Utilizing a modified A* algorithm, our method optimizes robot trajectories based on combined criteria of risk, distance, and uncertainty. Although preliminary validation is currently limited to static simulated conditions, this framework establishes a robust foundation for future applications in dynamic and real-world operational settings.