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| en:safeav:ctrl:sim [2025/10/22 02:19] – momala | en:safeav:ctrl:sim [2026/03/26 11:09] (current) – airi |
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| Building such twins is non-trivial. Our workflow constructs environment twins from aerial photogrammetry with RTK-supported georeferencing, then processes point clouds into assets capable of driving a modern simulator. The resulting model can be used across many AVs and studies, amortizing the cost of data collection and asset creation while preserving the fidelity needed for planning, perception, and control validation. | Building such twins is non-trivial. Our workflow constructs environment twins from aerial photogrammetry with RTK-supported georeferencing, then processes point clouds into assets capable of driving a modern simulator. The resulting model can be used across many AVs and studies, amortizing the cost of data collection and asset creation while preserving the fidelity needed for planning, perception, and control validation. |
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| | Digital twin and simulation ecosystems differ not only in fidelity and purpose across domains, but also in the **toolchains and platforms** that have emerged to support them. In **ground systems** (automotive, robotics), simulation is dominated by scalable, scenario-rich environments tightly coupled to AI/ML stacks. Widely used platforms include CARLA (open-source, Unreal Engine–based), NVIDIA DRIVE Sim (GPU-accelerated, synthetic data generation), PreScan and Simcenter (sensor-to-system validation), and MATLAB/Simulink for model-based design, SIL/HIL, and control validation. These platforms emphasize large-scale scenario generation, perception stack validation, and real-time or accelerated simulation with closed-loop autonomy. |
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| | In **airborne systems**, simulation platforms are more tightly aligned with certification workflows and high-fidelity physics. Common tools include X-Plane (used in research and some FAA-approved training contexts), Prepar3D, and engineering-grade environments such as ANSYS Fluent and MSC Adams for aerodynamics and flight dynamics. MATLAB/Simulink again plays a central role for flight control laws, avionics integration, and DO-178C/DO-331–aligned model-based development. These ecosystems support pilot-in-the-loop, avionics-in-the-loop, and increasingly autonomy-in-the-loop simulations with strong traceability. |
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| | For **marine systems**, simulation platforms reflect the importance of hydrodynamics, environmental disturbances, and long-duration operations. Representative tools include OrcaFlex (widely used for offshore structures and subsea systems), MOOS-IvP (common in autonomous underwater and surface vehicles), and Delft3D for simulating currents, sediment, and coastal processes. These are often coupled with control and navigation development in MATLAB/Simulink or ROS-based stacks. Compared to ground/air, marine simulations tend to trade interaction density for environmental realism and long-horizon mission modeling. |
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| | In space systems, simulation platforms are deeply rooted in astrodynamics, mission design, and high-fidelity subsystem modeling. Key tools include Systems Tool Kit (STK) for orbital analysis and mission planning, GMAT for trajectory optimization, and FreeFlyer. For system-level digital twins and MBSE integration, platforms such as Cameo Systems Modeler (SysML-based) and Simulink are widely used. These environments support mission rehearsal, fault analysis, and increasingly onboard autonomy validation, where simulation substitutes for otherwise impossible real-world testing. Across all four domains, a clear pattern emerges: **ground systems favor scale and data-driven simulation**, while **space systems prioritize first-principles fidelity**, with airborne and marine occupying structured intermediate points shaped by certification and environmental complexity. |
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| ====== From Scenarios to Properties: Making Requirements Executable ====== | ====== From Scenarios to Properties: Making Requirements Executable ====== |
