laijiajian / crl_kino

Using RL based controller for steering function of RRT. And a network learns to perform motion planning

This package is used to solve the kinodynamic problem using RRT.

Installation

Now it is still in development, to install it for development:

Notice that if you have installed tianshou, please remove it or create another virtual env to install this package. Because we use tianshou for reinforcemenr learning, and change it a bit for our use.

conda create -n crl_kino python=3.6 
conda activate crl_kino
git clone https://github.com/sldai/crl_kino.git
cd crl_kino
conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
pip install -e .

Use

Test:

# kinodynamic RRT using dwa as steering function
python example/test.py --case rrt

# kinodynamic RRT using a trained policy as steering function
python example/test.py --case rl_rrt

Gif of kinodynamic rrt results will be saved in the current folder.

Train an agent:

python examples/rl_training.py 

Results

RRT tree growing process

The final course