InfoFusion Controller: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning

This is the official implementation of our ICCE 2024 (Oral) paper InfoFusion Controller: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning.

Project Page

Abstract: In this paper, we propose the InfoFusion Controller, an advanced path planning algorithm that integrates both global and local planning strategies to enhance autonomous driving in complex urban environments. The global planner utilizes the informed Theta-Rapidly-exploring Random Tree Star (Informed-TRRT*) algorithm to generate an optimal reference path, while the local planner combines Model Predictive Control (MPC) and Pure Pursuit algorithms. Mutual Information (MI) is employed to fuse the outputs of the MPC and Pure Pursuit controllers, effectively balancing their strengths and compensating for their weaknesses.
The proposed method addresses the challenges of navigating in dynamic environments with unpredictable obstacles by reducing uncertainty in local path planning and improving dynamic obstacle avoidance capabilities. Experimental results demonstrate that the InfoFusion Controller outperforms traditional methods in terms of safety, stability, and efficiency across various scenarios, including complex maps generated using SLAM techniques.

Env Settings

conda env create -f planning.yaml
conda activate planning

export QT_QPA_PLATFORM=xcb
export PYTHONPATH="$PYTHONPATH:$PWD"

Compare Algorithms

Compare Route Planning Algorithms

Visualize performance by comparing route planning algorithms (the a_star, hybrid_a_star, theta_star, rrt_star, informed_rrt_star, infomed_rrt_star_smooth and informed_trrt_star)

python test/test_route_planner.py

Compare Controller Algorithms

Visualize performance by comparing controller algorithms (the pure_pursuit, mpc, adaptive_mpc, weighted_fusion, info_fusion)

python test/test_controller.py

Citation

If you consider our paper or code useful, please cite our paper:

@inproceedings{lee2025codebooknerf,
  title={InfoFusion Controller: Informed TRRT Star with Mutual Information based on Fusion of Pure Pursuit and MPC for Enhanced Path Planning},
  author={Seongjun Choi, Youngbum Kim, Nam Woo Kim, Mansun Shin, Byunggi Chae, Sungjin Lee},
  booktitle={ICCE},
  year={2025}
}