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--- en:safeav:as:general [2025/10/17 08:42] – agrisnik
+++ en:safeav:as:general [2025/10/17 08:57] (current) – [Middleware and Frameworks] agrisnik
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   * **MOOS-IvP:** A marine-oriented autonomy framework designed for mission planning and vehicle coordination in autonomous underwater and surface vehicles ((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)).
   * A**UTOSAR Adaptive Platform:** A standard architecture for automotive systems emphasising safety, reliability, and scalability ((AUTOSAR Consortium. (2023). AUTOSAR Adaptive Platform Specification. AUTOSAR)).
 These middleware platforms not only promote interoperability but also enforce architectural patterns that ensure predictable performance across heterogeneous domains.
+Most autonomous systems follow a hierarchical layered architecture:
+^ Layer ^ Function ^ Examples ^
+| Hardware Abstraction | Interface with sensors, actuators, and low-level control | Sensor drivers, motor controllers |
+| Perception | Process raw sensor data into meaningful environment representations | Object detection, SLAM |
+| Decision-Making / Planning | Generate paths or actions based on goals and constraints | Path planning, behavior trees |
+| Control / Execution | Translate plans into commands for actuators | PID, MPC, low-level control loops |
+| Communication / Coordination | Handle data sharing between systems or fleets | Vehicle-to-vehicle (V2V), swarm coordination |
+Depending on functional tasks system’s architecture is split into multiple layers to abstract functionality and technical implementation as discussed above. Below is a schema of a generic architecture to get a better understanding of typical tasks at different layers.
+<figure Generic Autonomous System Architectures>
+{{ :en:safeav:as:rtu_ch1_figure1.png?400| Edge IoT system' architecture}}
+<caption>Generic Autonomous System Architecture </caption>
+</figure>
+===== The Role of AI and Machine Learning =====
+Modern autonomous systems increasingly integrate machine learning (ML) techniques for perception and decision-making. Deep neural networks enable real-time object detection, semantic segmentation, and trajectory prediction ((LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444)). However, these data-driven methods also introduce architectural challenges:
+  * Increased computational load requiring edge GPUs or dedicated AI accelerators.
+  * The need for robust validation and explainability to ensure safety.
+  * Integration with deterministic control modules in hybrid architectures.
+Thus, many systems adopt hybrid designs, combining traditional rule-based or dynamics-based control with data-driven inference modules, balancing interpretability and adaptability

en/safeav/as/general.1760690578.txt.gz · Last modified: 2025/10/17 08:42 by agrisnik