Quadcopter 3D Simulator

The project simulates a quadcopter in 3D environment. It contains a basic quadcopter dynamics model, hover controller, trajectory generator, visualisation toolkit and a top level scheduler which runs each module at a specific rate.

PID Controller

LQR Controller

Differential Flatness based controller (euler angle orientation representation)

Quarotor model considering rotor drag

Rotor drag is modeled as in reference [1] for rigid propellers. Adding this important effect results in a more realistic simulation of the drone dynamics. Also, the performance of control algorithms considering aerodynamic effects can be assesed.

Differential Flatness based controller with no rotor drag aerodynamics consideration

Differential Flatness based controller with velocity-only dependent rotor drag [2]

Differential Flatness based controller with velocity-and-thrust dependent rotor drag

Motivation

I have been playing and studying quadcopter in my spare time since 2014 when I first bought a crazyflie. There are many interesting projects around already, like vision-based SLAM, hover control and advance manoeuvre, etc. However, there are very few open source quadcopter simulator which helps a beginner to overcome the mental barrier of understanding the underlying physics. There are a lot of research papers on the topic of quadcopter control and autonomous application, but none of those can be made possible without a decent simulation tool. This project aims to address that. Thanks to Coursera's online course Aerial Robotics by Professor Vijay Kumar, which presents quadcopter's motion equations in detail, I was then inspired and finally able to write this from scratch.

Install

The simulator uses numpy, matplotlib, control, slycot and scipy.

1. Install git.
2. In the location you prefer, open a terminal and:

git clone https://github.com/juanmed/quadcopter-simulation
python -m pip2 install -r requirements.txt

To Run

python runsim.py

Future Work

1. add sensor noise model, wind.
2. add sensor fusion.i.e EKF,UKF
3. reinforment learning.

Contribution

Any contribution are welcome if you are interested in the project, but please let me know.

References

[1] Kai, J. M., Allibert, G., Hua, M. D., & Hamel, T. (2017). Nonlinear feedback control of Quadrotors exploiting First-Order Drag Effects. IFAC-PapersOnLine, 50(1), 8189-8195. https://doi.org/10.1016/j.ifacol.2017.08.1267 [2] Faessler, M., Franchi, A., & Scaramuzza, D. (2017). Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories. https://doi.org/10.1109/LRA.2017.2776353

Contact

Peter Huang

Email: hbd730@gmail.com