MAVS provides the ability to evaluate the performance of autonomous perception and navigation software in real-time. The software is built with an MPI-based framework for coupling parallel processes, as well as a physics-based sensor simulator for LIDAR, GPS, cameras and other sensors.

While MAVS is a fully functional standalone simulator, additional wrappers allow MAVS to be integrated with robotic development tools such the Robotic Operating System (ROS).

A detailed view of a variety of factors that impact simulated environments

A detailed view of a variety of factors that impact simulated environments.

Why use simulation for autonomous ground vehicle testing? The image above displays the many interactions that are involved in developing a simulation. Controlling these factors contributes greatly to experimental safety and cost effectiveness, repeatability, and the ability to automatically perform thousands of experiments.

What importance does MAVS bring to simulation software?

  • Optimized for off-road activity: MAVS uses a validated tire-soil interaction model for sand and clay and uses a validated lidar-vegetation interaction model
  • Optimized for large-scale HPC simulation: MAVS automatically generates new off-road trains, and natively runs on all flavors of Unix/Linux , as well as Windows
  • Optimized for ease of use: MAVS interfaces with ROS and has a 0-install python scripting interface

While development is ongoing, the MAVS software is currently being used for applications including sensor placement, synthetic training-data for neural networks, and education efforts. MSU’s Halo Project also incorporates the MAVS software for autonomy system-level testing.

Screen captures from MAVS simulations showing differing environments

Screen captures from MAVS simulations that show the same environment with differing ecosystems (top) and with a change in terrain roughness (bottom).

Screen captures from MAVS software exhibiting the environment properties

Screen captures from MAVS software exhibiting the following variety of environment properties (from L to R): night, haze, snow, fog, dust, and rain.

The environment within MAVS can demonstrate a variety of custom ecosystems and natural properties, including snow, fog, dust and rain. Terrain properties and roughness can be controlled, as well as daylight brightness, haze, and moonlight.