Drone Examples

This directory contains examples of drone simulations using the Genesis framework.

Available Examples

1. Interactive Drone (interactive_drone.py)

A real-time interactive drone simulation where you can control the drone using keyboard inputs:

• ↑ (Up Arrow): Move Forward (North)
• ↓ (Down Arrow): Move Backward (South)
• ← (Left Arrow): Move Left (West)
• → (Right Arrow): Move Right (East)
• Space: Increase Thrust (Accelerate)
• Left Shift: Decrease Thrust (Decelerate)
• ESC: Quit

Run with:

python interactive_drone.py -v -m

2. Automated Flight (fly.py)

A pre-programmed drone flight simulation that follows a predefined trajectory stored in fly_traj.pkl.

Run with:

python fly.py -v -m

3. Hover Environment (hover_env.py, hover_train.py, hover_eval.py)

The hover environment (hover_env.py) is designed to train a drone to maintain a stable hover position by reaching randomly generated target points. The environment includes:

• Initialization of the scene and entities (plane, drone and target).
• Reward functions to provide feedback to the agent based on its performance in reaching the target points.
• Command resampling to generate new random target points and environment reset functionalities to ensure continuous training.

Acknowledgement: The reward design is inspired by Champion-level drone racing using deep reinforcement learning (Nature 2023)

3.0 Installation

At this stage, we have defined the environments. Now, we use the PPO implementation from rsl-rl to train the policy. First, install all Python dependencies via pip:

pip install tensorboard "rsl-rl-lib>=5.0.0"

3.1 Training

Train the drone hovering policy using the HoverEnv environment.

Run with:

python hover_train.py -e drone-hovering -B 8192 --max_iterations 301

Train with visualization:

python hover_train.py -e drone-hovering -B 8192 --max_iterations 301 -v

3.2 Evaluation

Evaluate the trained drone hovering policy.

Run with:

python hover_eval.py -e drone-hovering --ckpt 300 --record

Note: If you experience slow performance or encounter other issues during evaluation, try removing the --record option.

For the latest updates, detailed documentation, and additional resources, visit this repository: GenesisDroneEnv.

4. Quadcopter Controller (quadcopter_controller.py & fly_route.py)

A PID-based controller to provide stable point-to-point flight without controlling each rotor. Parameters have been tuned for controlled flight but not optimized. Stored in fly_route.py. Run fly_route.py to test.

Run with:

python fly_route.py

Technical Details

• The drone model used is the Crazyflie 2.X (urdf/drones/cf2x.urdf)
• Base hover RPM is approximately 14468
• Movement is achieved by varying individual rotor RPMs to create directional thrust
• The simulation uses realistic physics including gravity and aerodynamics
• Visualization is optimized for macOS using threaded rendering when run with -m flag

Controls Implementation

The interactive drone uses differential RPM control:

• Forward/Backward movement: Adjusts front/back rotor pairs
• Left/Right movement: Adjusts left/right rotor pairs
• All movements maintain a stable hover while creating directional thrust
• RPM changes are automatically clipped to safe ranges (0-25000 RPM)