| A key enabler of this transformation is the system’s architecture — the structured framework that defines how system components interact, communicate, and evolve. In autonomous systems, architecture governs how sensor data is interpreted, how decisions are made in uncertain environments, and how control actions are executed safely and reliably. For instance, in self-driving cars, architectural layers coordinate LiDAR, camera, and radar inputs to produce real-time navigation decisions; in drones, they manage flight stability and mission autonomy; and in underwater robots, they handle communication delays and localisation challenges ((Benjamin, M. R., Curcio, J. A., & Leonard, J. J. (2012). MOOS-IvP autonomy software for marine robots. Journal of Field Robotics, 29(6), 821–835. https://doi.org/10.1002/rob.21455))((Corke, P., Roberts, J., & Sukkarieh, S. (2017). Networked robotics: Building large-scale autonomy. Annual Reviews in Control, 43, 19–35)). | A key enabler of this transformation is the system’s architecture — the structured framework that defines how system components interact, communicate, and evolve. In autonomous systems, architecture governs how sensor data is interpreted, how decisions are made in uncertain environments, and how control actions are executed safely and reliably. For instance, in self-driving cars, architectural layers coordinate LiDAR, camera, and radar inputs to produce real-time navigation decisions; in drones, they manage flight stability and mission autonomy; and in underwater robots, they handle communication delays and localisation challenges ((Benjamin, M. R., Curcio, J. A., & Leonard, J. J. (2012). MOOS-IvP autonomy software for marine robots. Journal of Field Robotics, 29(6), 821–835. https://doi.org/10.1002/rob.21455))((Corke, P., Roberts, J., & Sukkarieh, S. (2017). Networked robotics: Building large-scale autonomy. Annual Reviews in Control, 43, 19–35)). |
