- The paper presents AirSim, a novel simulator that combines high-frequency physics with visual realism to enable safe, real-time autonomous vehicle testing.
- The paper demonstrates robust validation through quadrotor experiments, achieving Hausdorff distances as low as 0.65m compared to real flights.
- By offering a modular architecture and extensible sensor models, the simulator paves the way for future research in control systems, SLAM, and reinforcement learning.
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
The paper "AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles" presents an advanced simulator designed to aid in the development and testing of algorithms for autonomous vehicles. The simulator is built on the Unreal Engine and aims to provide both visually and physically realistic simulations. This is necessary due to the high cost and potential dangers associated with real-world testing of autonomous vehicles.
Key Contributions
The primary contributions of AirSim include a high-frequency physics engine suitable for real-time hardware-in-the-loop (HITL) simulations, support for popular communication protocols such as MavLink, and a modular design that allows for extensibility and usability in other projects. The simulator is demonstrated using a quadrotor, which was experimentally compared with real-world flights to validate the software's accuracy.
Technical Overview
Core Architecture
The simulator's architecture is modular and includes multiple core components: environment model, vehicle model, physics engine, sensor models, rendering interface, public API layer, and an interface for vehicle firmware. This modularity enables extensibility, allowing new types of vehicles, hardware platforms, and software protocols to be integrated.
Vehicle Model
AirSim represents vehicles as rigid bodies with a collection of vertices. It computes the forces and torques generated by these vertices to simulate vehicle dynamics. This model allows for the simulation of complex vehicles like quadrotors and VTOLs by adjusting parameters like mass, inertia, friction coefficients, and more.
Environment Modeling
The environment model simulates various physical phenomena such as gravity, air density, air pressure, and the Earth's magnetic field. The gravity model accounts for altitude variations, while the magnetic field model uses the tilted dipole approximation for realistic simulation. Air pressure and density are computed using models from atmospheric science.
Physics Engine
AirSim's physics engine computes the next kinematic state of a body using data on forces and torques. It employs algorithms like the Velocity Verlet integration method for numerical stability and employs quaternion algebra for orientation updates. Collision detection is handled by Unreal Engine’s system, providing impact response and Coulomb friction calculations.
Sensors
The simulator models several sensors, including accelerometers, gyroscopes, barometers, magnetometers, and GPS. Each sensor model adds noise and bias drift based on real-world data, making the sensor outputs more realistic. For instance, the barometer model uses Gaussian Markov processes to simulate long-term drift, and the gyroscope and accelerometer models include biases and noise parameters.
Experimental Validation
The authors validate AirSim by comparing simulated flights with real-world experiments using a quadrotor. Two different flight patterns, a square and a circle, were tested. The comparison showed close alignment between the simulated and real-world trajectories, with Hausdorff distances of 0.65m and 1.47m respectively. Additionally, individual sensor models for the barometer, magnetometer, and IMU were evaluated and found to closely match the characteristics of their real-world counterparts.
Implications and Future Work
AirSim has significant implications for the development of autonomous vehicle algorithms. It allows for extensive testing and training in a risk-free virtual environment, which can accelerate the development and deployment of autonomous systems. The simulator's high fidelity and rich API support create opportunities for research in reinforcement learning, SLAM, and advanced control systems.
The authors suggest several areas for future improvement, including richer collision models, advanced camera sensor simulations, GPS signal degradation simulation, and enhanced wind and thermal effects modeling. These advancements would further narrow the gap between simulation and reality, enhancing the utility of AirSim as a development tool.
Conclusion
AirSim stands out as a versatile tool for the development and testing of autonomous vehicles, thanks to its high-fidelity simulations and modular design. The robust validation of simulated results against real-world data underscores its reliability. By addressing the outlined future work, AirSim could further cement its status as a crucial resource in the autonomous vehicle research community.