Scaling SADE (Safety Aware Drone Ecosystem): A Hybrid UAV Simulation System for High-Fidelity Research

Autonomous drones are moving into increasingly complex, real-world environments where safety, compliance, and reliability have to be built in from the start. That's the motivation behind SADE—the Safety Aware Drone Ecosystem. SADE brings together physics-accurate simulation and photorealistic rendering, paired with PX4-based autonomy, to give researchers and developers a realistic, repeatable space to design, test, and validate UAV behavior before it ever leaves the lab.

Under the hood, SADE combines Gazebo for flight dynamics and physics with Unreal Engine 5 for immersive visuals, augmented by Cesium for globe-scale terrain that closely mirrors real geography. PX4 anchors flight control, and Mavlink backed by a "Mavlink Router" keeps telemetry and commands flowing reliably across multiple endpoints. This setup makes it possible to stream camera feeds from the simulated environment back into the autopilot loop, exercise computer vision pipelines under challenging conditions, and verify perception-driven behaviors in a safe, controlled feedback cycle.

The architecture is designed for scale and clarity. A React front end, integrated with Cesium-JS, lets users define zones, geometry, and mission parameters visually. Those configurations are saved by the backend and published to an MQTT broker, which the workflow runner listens to. From there, the runner launches isolated Docker Compose networks to run concurrent simulations without interference. In these stacks, PX4, Gazebo, pose-sending utilities, and shepherd tools (suas-engine) coordinate flight behavior, while an Unreal Engine container—accelerated via NVIDIA's container toolkit—renders the world. A Pixel Streaming signaling server brings UE5 to the browser, and the Mavlink Router consolidates telemetry to a single ground control station for consistent ops.

One of SADE's most useful ideas is the "SADE Zone." These are policy-aware regions that translate rules directly into drone behavior—think altitude caps, no-capture constraints for sensitive areas, or mandatory permission requests before entering and when exiting. Instead of treating compliance as something to bolt on later, SADE embeds policy into the simulation itself so drones "self-behave" according to the rules of the environment. That blend of physical realism and operational policy is essential for regulated and safety-critical use cases.

From a user standpoint, the SADE UI is where everything comes together. It gives you a flexible way to draw zones, set rules, and turn those constraints into autonomous responses. By capturing precise coordinates and dimensions, the UI drives compliance automatically, freeing teams to focus on algorithm design, perception, and mission planning. This approach is especially powerful for rare or risky scenarios that are hard to reproduce in the field but vital for building robust autonomy. SADE is built to handle multiple users and runs at once, intelligently distributing CPU and GPU resources so experiments remain responsive at scale. Looking ahead, the roadmap emphasizes richer workflows, larger multi-vehicle scenarios, and deeper policy enforcement to narrow the gap between simulation and real operations.

If you're working on UAV autonomy, simulation tooling, or regulated operations and want to explore policy-aware zones in photo real environments, check out the demo: https://sade.crc.nd.edu/