SANIS-HITSZ/LAESim

面向空天地海协同研究的开源多载具仿真平台，支持无人机、车辆、舰船、卫星、ROS 与可选 ns-3 自组织网络。

What it solves

LAESim provides a unified simulation environment for coordinating multiple types of vehicles—including drones, ground vehicles, ships, and satellites—across air, space, land, and sea domains. It bridges the gap between high-fidelity visual simulation, realistic orbital mechanics for satellites, and complex network communication constraints, allowing researchers to test multi-agent coordination and navigation in a single integrated workflow.

How it works

Built as an extension of Microsoft AirSim and Unreal Engine 4.27, LAESim integrates several specialized modules:

  • Hybrid Vehicle Simulation: Supports diverse vehicle types (SimpleFlight, PhysXCar, SimpleBoat, SimpleSatellite) within the same scene.
  • SceneMap: Converts satellite or task images into collidable 3D maps in Unreal Engine, mapping pixel coordinates to GPS and metric coordinates.
  • Space Mission Bridge: Uses TLE/SGP4, CSV, or Orekit to calculate real satellite orbits and visibility windows, while UE handles the visual representation.
  • Network Backend: Offers switchable communication modes, from ideal communication to high-fidelity ns-3 simulations for Wi-Fi ad hoc and satellite-to-ground/satellite-to-satellite links with realistic propagation delays and link budgets.
  • RadioMapNav: Utilizes Sionna RT to generate offline channel maps (RSS/SINR) for communication-constrained path planning.

Who it’s for

It is designed for researchers and engineers working on multi-vehicle coordination, aerospace network simulation, satellite-ground communication, and autonomous navigation under communication constraints.

Highlights

  • Cross-Domain Coordination: Simultaneous simulation of drones, cars, boats, and satellites.
  • Integrated Network Simulation: Integration with ns-3 for realistic packet-level network behavior and link budget analysis.
  • Image-to-Map Conversion: Ability to turn 2D images into 3D collidable environments with GPS registration.
  • Realistic Space Tasks: Support for TLE/SGP4 and Orekit for accurate orbital analysis and coverage windows.
  • Communication-Aware Navigation: Tools for generating radio maps and planning paths based on signal quality (SINR).

Related

  • Project
    iamaisim/ProjectAirSim

    Project AirSim is an open‑source simulation platform for autonomous drones, aircraft, and ground vehicles. It can run headlessly (no Unreal) or inside Unreal Engine 5 for full 3D worlds and rendered sensors. Features include multiple physics back‑ends (Fast, JSBSim, Simulink), support for PX4/ArduPilot SITL/HITL, a rich sensor suite (cameras, LiDAR, radar, IMU, GPS, etc.), Python/C++/ROS 2 APIs, and multi‑vehicle/swarm capabilities. The project is maintained by IAMAI Consulting, builds on Microsoft’s AirSim code, and is MIT‑licensed.

  • Project
    microsoft/AirSim

    An open-source simulator for drones and cars built on Unreal Engine, designed as a platform for AI research in deep learning, computer vision, and reinforcement learning.

  • Project
    Cosys-Lab/Cosys-AirSim

    A high-fidelity simulation framework for drones and cars built on Unreal Engine, providing realistic sensor simulation and hardware-in-the-loop support for autonomous vehicle research.

  • Project
    Zihao-Felix-Zhou/UavNetSim

    A Python-based simulation platform for UAV communication networks that models the full networking stack, from physical radio propagation to RL-based routing protocols.

  • Project
    louiszengCN/CarlaAir

    A unified simulation infrastructure that integrates CARLA and AirSim into a single process to enable high-fidelity, synchronized air-ground embodied intelligence research.