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/ Astraion Platform

Three pillars. One simulation infrastructure layer.

High-fidelity environments, a production-ready RL toolchain, and sensor-accurate digital twins — instrumented as a unified platform so your autonomous systems validate interface contracts before a single flight hour.

Extreme close-up of a sensor fusion visualization panel — cyan LiDAR point cloud rings expanding from a drone silhouette at center, radar return arcs in pale silver overlaid on a dark graphite UI surface, thin GPS trace lines crossing the frame, cool studio lighting, no humans
Extreme close-up of a sensor fusion visualization panel — cyan LiDAR point cloud rings expanding from a drone silhouette at center, radar return arcs in pale silver overlaid on a dark graphite UI surface, thin GPS trace lines crossing the frame, cool studio lighting, no humans
— Simulation Stack

Sensor-accurate simulation loop

GPS, LiDAR, and radar fusion run inside the simulation loop at hardware-representative noise profiles. ROS2 message interfaces are preserved end-to-end so hardware teams validate interface contracts without a physical aircraft.

Multi-sensor fusion fidelity means the autonomy stack sees the same degraded, noisy world it will encounter in flight — procedurally varied across thousands of scenario seeds.

Interfaces: ROS2 native · GPS / LiDAR / Radar fusion · Configurable noise profiles · 1000 Hz sensor tick rate

Wide overhead isometric view of procedurally generated European coastal industrial zone — photorealistic terrain with port infrastructure, wind turbines, and a river delta visible, thin cyan bounding box annotations overlaid on vehicles and structures, overcast cool daylight, no humans, deep blue-grey sky
Wide overhead isometric view of procedurally generated European coastal industrial zone — photorealistic terrain with port infrastructure, wind turbines, and a river delta visible, thin cyan bounding box annotations overlaid on vehicles and structures, overcast cool daylight, no humans, deep blue-grey sky
— Digital Twin Engine

Sub-meter terrain. Procedural weather. Labeled data.

Real European terrain replicated at sub-meter resolution from satellite and LiDAR survey sources. Procedural weather layers — cloud cover, precipitation, fog — generate sensor-relevant degradation without manual scene authoring.

Synthetic datasets export with 3D bounding box annotations, instance segmentation masks, and per-frame sensor metadata — ready for model training pipelines without post-processing.

Close-up of a reinforcement learning training dashboard rendered on a dark cockpit-glass surface — loss curves and reward graphs in thin cyan lines, multi-agent episode replay timeline at bottom, distributed training node grid in silver, cool studio lighting, no humans, precise engineering UI aesthetic
Close-up of a reinforcement learning training dashboard rendered on a dark cockpit-glass surface — loss curves and reward graphs in thin cyan lines, multi-agent episode replay timeline at bottom, distributed training node grid in silver, cool studio lighting, no humans, precise engineering UI aesthetic
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— AI & RL Stack

Edge-ready from the first training epoch

PPO and SAC policies train inside the simulation loop with multi-agent distributed rollouts. Edge deployment constraints — memory budget, inference latency, quantization targets — are enforced as training objectives, not retrofit steps.

Trained policies export with hardware-specific manifests for common embedded compute platforms used in civil aerospace and research-grade UAV programs.

Algorithms: PPO · SAC · Multi-agent distributed training · Edge deployment manifest — instrumented at training time, not exported as an afterthought.