GP-MPPI

This repository contains the ROS implementation of the GP-MPPI control strategy, an online learning-based control strategy that integrates the state-of-the-art sampling-based Model Predictive Path Integral (MPPI) controller with a local perception model based on Sparse Gaussian Process (SGP).

The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, enabling the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function for the local MPPI planner. MPPI then computes the optimal control sequence that satisfies the robot and collision avoidance constraints. This approach eliminates the need for a global map of the environment or an offline training process.

Paper:

If you find this code useful in your research, please cite:

Ihab S. Mohamed, Mahmoud Ali, and Lantao Liu. "GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments." IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.

Paper: https://arxiv.org/abs/2306.12369
Video: https://youtu.be/et9t8X1wHKI

Bibtex:

@article{mohamed2023gp,
  title={GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments},
  author={Mohamed, Ihab S and Ali, Mahmoud and Liu, Lantao},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2023}
}

Software Requirements and Installation Instructions:

Please refer to the provided guidelines in our log-MPPI repository for detailed instructions.

ROS Packages:

jackal_ros

The jackal_ros package contains the common packages for Jackal, including Gazebo simulator with various world definitions and their ROS launch files, robot description, messages, and controllers.

gp_subgoal

The gp_subgoal package utilizes the SGP occupancy model to learn the navigable space around the robot, identifies a set of suggested subgoals, and recommends the optimal subgoal to the MPPI local planner by minimizing a predefined cost function.

mppi_control

The mppi_control node encompasses the implementation of vanilla MPPI, log-MPPI, and GP-MPPI control strategies. It subscribes to: (i) the 2D local costmap generated by the robot's onboard sensor for achieving collision-free navigation and (ii) the recommended GP-subgoal published by the gp_subgoal node. Subsequently, it publishes the control commands, specifically the linear and angular velocities of the robot.

velodyne_simulator

URDF description and Gazebo plugins to simulate Velodyne laser scanners. To enhance the real-time performance of the GP-subgoal recommender node (gp_subgoal), we've reduced the sample count to 500 (namely, samples:=500) from the original 1875 in VLP-16.urdf.xacro.

Usage:

To initiate the Gazebo simulation with Jackal in the provided cluttered environment, execute:
```
roslaunch jackal_gazebo world_stage.launch env_name:=maze1
```
- maze1 is a maze-like environment spanning 20 meters by 20 meters, featuring three U-shaped rooms (U1, U2, and U3), along with several additional obstacles.
- forest1 is a forest-like environment measuring 50 meters by 50 meters, with a tree density of 0.2 trees per square meter. To create your own forest-like environment, please utilize the forest_gen package.
To start the GP-subgoal recommender policy, run:
```
roslaunch  gp_subgoal gp_subgoal_sim.launch
```
- Note that this package is only activated when GP-MPPI is executed, i.e., when the gp_mppi argument in the following comment is set to true.
To start the MPPI, log-MPPI, or GP-MPPI control strategies, 2D costmap node, and visualization of the system, run:
- For MPPI, run:
```
roslaunch mppi_control control_stage.launch normal_dist:=true gp_mppi:=false
```
- For log-MPPI, run:
```
roslaunch mppi_control control_stage.launch normal_dist:=false gp_mppi:=false
```
- To execute GP-MPPI with the sample mode (SM), run:
```
roslaunch mppi_control control_stage.launch gp_mppi:=true recovery_mode:=false
```
To enable the recovery mode (RM), set recovery_mode to true.

Primary code maintainer:

Ihab S. Mohamed and Mahmoud Ali (e-mail: {mohamedi, alimaa}@iu.edu)
Vehicle Autonomy and Intelligence Lab (VAIL)
Indiana University - Bloomington, USA

IhabMohamed/GP-MPPI