# Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation

arXiv: https://arxiv.org/abs/2307.05263 · HTML: https://mlsanity.com/paper/2307.05263/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2023.

## Built on by (most cited first, 10 of 10)

- [SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum](https://mlsanity.com/paper/2412.16346/) (2024)
- [Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description](https://mlsanity.com/paper/2412.01398/) (2024)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [VIRTUS-FPP: Virtual Sensor Modeling for Fringe Projection Profilometry in NVIDIA Isaac Sim](https://mlsanity.com/paper/2509.22685/) (2025)
- [WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning](https://mlsanity.com/paper/2607.01281/) (2026)
- [Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid](https://mlsanity.com/paper/2505.03694/) (2025)
- [AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning](https://mlsanity.com/paper/2601.15614/) (2026)
- [Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning](https://mlsanity.com/paper/2605.31119/) (2026)
- [Guided Diffusion with Distilled Vision-Language Reliability for Aerial Navigation](https://mlsanity.com/paper/2606.13883/) (2026)
- [Barrier-Shaped Recurrent Reinforcement Learning for Autonomous Landing on a Heaving Ship Deck](https://mlsanity.com/paper/2610.03924/) (2026)

## Abstract (arXiv)

Developing and testing novel control and motion planning algorithms for aerial vehicles can be a challenging task, with the robotics community relying more than ever on 3D simulation technologies to evaluate the performance of new algorithms in a variety of conditions and environments. In this work, we introduce the Pegasus Simulator, a modular framework implemented as an NVIDIA Isaac Sim extension that enables real-time simulation of multiple multirotor vehicles in photo-realistic environments, while providing out-of-the-box integration with the widely adopted PX4-Autopilot and ROS2 through its modular implementation and intuitive graphical user interface. To demonstrate some of its capabilities, a nonlinear controller was implemented and simulation results for two drones performing aggressive flight maneuvers are presented. Code and documentation for this framework are also provided as supplementary material.