/LPVTI-Offroad-Navigation

Primary LanguageC++MIT LicenseMIT

Learning Predictive Vehicle-Terrain Interaction for Safe Off-Road Navigation

A safe, efficient, and agile ground vehicle navigation algorithm for 3D off-road terrain environments.

This GitHub repository is currently under construction, and we will be updating it with comprehensive instructions on setting up the environment and running the code.

System architecture

We design a system that learns the terrain-induced uncertainties from driving data and encodes the learned uncertainty distribution into the traversability cost for path evaluation. The navigation path is then designed to optimize the uncertainty-aware traversability cost, resulting in a safe and agile vehicle maneuver.

Dependency

Tested with ROS Noetic, torch 1.12.0+cu116, gpytorch 1.8.0

  1. To install Gazebo simulation environment -> Please follow the installation tutorial in "https://github.com/AutoRally/autorally.git"
  2. For Mapping modules -> Dependencis can be found at "https://github.com/leggedrobotics/traversability_estimation.git"
  3. NVIDIA Graphic card and Gpytorch is required
pip install gpytorch
  1. pytorch is required -> https://pytorch.org/get-started/locally/

Install

Use the following commands to download and compile the package.

cd ~/catkin_ws/src
git clone https://github.com/HMCL-UNIST/LPVTI-Offroad-Navigation.git 
cd ..
catkin build

Run the package

  1. Run the Gazebo Simulation(e.g., environment with mud area) :
roslaunch autorally_gazebo mud_path.launch
  1. Run the preprocessing Modules :
roslaunch elevation_mapping elevation_mapping.launch
roslaunch traversability_estimation traversability_estimation.launch
  1. Run the predictive vehicle-terrain interaction-aware path planning Module :
roslaunch pvti_offroad main.launch

3-1. (To collect training data, Run below instead of main.launch)

roslaunch pvti_offroad data_logging.launch
  1. Run low level controller
roslaunch lowlevel_ctrl lowlevel_ctrl.launch

Acknowledgement

I would like to express my sincere thanks to following

  • Our 3D Simulation environment and the Gazebo vehicle model is based on Autorally research platform
(Goldfain, Brian, et al. "Autorally: An open platform for aggressive autonomous driving." IEEE Control Systems Magazine 39.1 (2019): 26-55.)
  • Elevation and traversability mapping modules used in preprocessing step are based on these awesome work.
( P. Fankhauser, M. Bloesch, C. Gehring, M. Hutter, and R. Siegwart,
        “Robot-centric elevation mapping with uncertainty estimates,” in Mobile
          Service Robotics. World Scientific, 2014, pp. 433–440.) 

(P. Fankhauser, M. Bloesch, and M. Hutter, “Probabilistic terrain
mapping for mobile robots with uncertain localization,” IEEE Robotics
and Automation Letters, vol. 3, no. 4, pp. 3019–3026, 2018.)