PteroSim provides a UAV flight simulation platform built on Unreal Engine 5, enabling developers to model any airframe, sensor suite, and autopilot such as PX4 or Ardupilot.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and testing UAV autonomy requires realistic flight dynamics, sensor models, and autopilot integration, but physical testing is time‑consuming, costly, and limited in scale. Engineers also need to evaluate coordination and collision‑avoidance for large fleets, which is difficult to replicate with traditional single‑drone simulators.
Solution
PteroSim offers a high‑fidelity UAV flight simulation platform built on Unreal Engine 5 that lets developers import any airframe, sensor suite, and autopilot stack such as PX4 or Ardupilot. The engine delivers precise multi‑rotor, fixed‑wing, VTOL, and helicopter dynamics while running simulations 6‑10× faster than real time, accelerating training, validation, and testing cycles. Users can create urban, windy, or sensor‑noisy environments to stress‑test autonomy algorithms before real‑world deployment. The platform supports dozens of concurrent instances, enabling large‑scale fleet coordination, formation flying, and collision‑avoidance scenarios within a single synchronized environment. Results can be exported for further analysis, providing a complete end‑to‑end autonomy testing pipeline.
Target Audience
Primary customers are UAV manufacturers, autonomous drone developers, and research teams that need rapid, high‑fidelity simulation for flight‑control validation and fleet‑level testing.
Features
- Unreal Engine 5 core delivering photorealistic environments and physics‑accurate flight dynamics for multi‑rotor, fixed‑wing, VTOL, and helicopter drones
- Direct integration with PX4 and Ardupilot flight stacks, mirroring real‑world control loops and sensor feeds
- Simulation speed of 6‑10× real time to compress training and validation timelines
- Multi‑instance architecture supporting 100+ agents and mixed‑fleet scenarios for coordination and collision‑avoidance testing
- Configurable environmental conditions (urban layouts, wind fields, sensor noise) to evaluate robustness of autonomy algorithms
- Exportable flight logs and sensor data for offline analysis and machine‑learning pipeline integration