====== Ground, Aerial, and Marine Vehicle Architectures ======
{{:en:iot-open:czapka_b.png?50| Bachelors (1st level) classification icon }}


====== Introduction ======

Over the past two decades, the rapid evolution of digital technologies has transformed the design, deployment, and operation of autonomous systems. The advancements in artificial intelligence (AI), robotics, and advanced sensors have driven the emergence of intelligent platforms, which, depending on their application domain and specifics, are capable of operating with limited or no human intervention. This transformation spans across ground, aerial, and marine environments — each presenting distinct challenges yet sharing a common architectural foundation centred on perception, decision-making, and control ((Thrun, S. (2010). Toward robotic cars. Communications of the ACM, 53(4), 99–106. https://doi.org/10.1145/1721654.1721679))((Raj, A., & Saxena, P. (2022). Emerging trends in autonomous systems architecture: Challenges and opportunities. IEEE Access, 10, 54321–54345.)). Currently, systems are no longer perceived and designed as isolated machines but integral parts of a broader digital ecosystem involving cloud computing, edge processing, and distributed intelligence ((Lee, E. A., Seshia, S. A., & Edwards, S. (2020). Introduction to Embedded Systems: A Cyber-Physical Systems Approach (3rd ed.). MIT Press)). The ongoing Industry 4.0 and emerging Industry 5.0 initiatives emphasise the fusion of human–machine collaboration, sustainability, and adaptability, all of which depend heavily on robust and modular system architectures.
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)).
Further, the architectures of the autonomous systems and the related topics  are discussed in the following order:

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  * [[en:safeav:as:general]]
  * [[en:safeav:as:typical]]
  * [[en:safeav:as:refarchitectures]]
  * [[en:safeav:as:applicationdomains]]
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