Welcome to the repository for the Python code used for the paper:

"Inclined Quadrotor Landing using Deep Reinforcement Learning"

By Jacob Kooi and Robert Babuska.

More documentation will be added soon.

InclinedDroneLander

Model-free training of 2d inclined landing and 3d setpoint-tracking for the Crazyflie 2.1 Nano-UAV. The resulting policies are transferable to use on a real crazyflie. (See https://youtu.be/53YaqfwUIFU)

Usage

• In main.py, select either 2d inclined landing or 3d setpoint training.
• When choosing setpoint tracking, one can use SAC,TD3,A2C, PPO etc.
• When choosing inclined landing, use the on-policy PPO algorithm.
• Select the thrust action space, taken as a PWM signal from hover (around 42000 for m = 0.3303). Also select the timesteps to train for and the sampling time (1/50s recommended).
• run main.py

Dependencies

Trained and tested on:

Python 3.7
PyTorch 1.5.0
Numpy 1.18.4
StableBaselines3 0.6.0
gym 0.17.3

Results

PPO Sparse Inclined Landing (3000000 timesteps)	PPO Euclidean Setpoint Tracking (1000000 timesteps) (Left is xz-plane, right is yz-plane)

Questions

For any questions, errors or suggestions contact me at jacobkooi92@gmail.com.

Citing

When using this code or referring to our work please cite the paper at:

https://ieeexplore.ieee.org/document/9636096/citations?tabFilter=papers#citations

Or use this bibtex:

@INPROCEEDINGS{9636096,

author={Kooi, Jacob E. and Babuška, Robert},

booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

title={Inclined Quadrotor Landing using Deep Reinforcement Learning},

year={2021},

volume={},

number={},

pages={2361-2368},

doi={10.1109/IROS51168.2021.9636096}}