Simulation environments for
drones

UAV autonomy training needs cluttered, realistic environments — buildings, trees, wires, delivery targets, dynamic obstacles. Rigyd generates physics-enabled OpenUSD assets compatible with Isaac Sim, AirSim, and Gazebo.

The problem

Why existing workflows fall short.

Aerial environments are visually complex

Drones operate in dense, varied scenes: urban canyons, warehouse interiors, forests, mountainsides. Each environment type needs thousands of unique, varied 3D assets.

Lightweight physics still needs accuracy

UAV dynamics are sensitive to aerodynamic interactions with obstacles. Mass distribution and collision-mesh detail affect propeller wash modeling and rotor-wake-aware path planning.

Perception stacks need semantic variety

Vision-based drone autonomy trains on diverse object classes. Lack of semantic and geometric diversity caps policy robustness the moment a drone enters a new environment.

How Rigyd helps

AI-native infrastructure that automates the hard parts.

Dense environment catalogs

Convert entire 3D building, infrastructure, and landscape catalogs into SimReady assets — useful for urban delivery, infrastructure inspection, and search-and-rescue training.

Wire- and edge-aware collision geometry

Convex decomposition captures wing-catching edges, antenna-snagging protrusions, and wire-like thin geometry — critical for realistic collision-avoidance policy training.

Semantic labels for perception

Each asset ships with semantic labels (building, vegetation, vehicle, person), enabling perception-in-the-loop UAV policy training without a separate annotation pass.

97%

cost reduction vs hand-modeled drone environments

40%

better real-world performance with accurate physics

~5 min

per asset, from upload to simulation-ready

Build drone training environments at scale

Upload 3D obstacle and environment models and get SimReady assets for UAV simulation.

Starts at $29/month. 30 SimReady objects included.