DDPG-AirSim-Drone-Obstacle-Avoidance

Using DDPG and ConvLSTM to control a drone to avoid obstacle in AirSim

This project is developed based on Microsoft-Airsim, achieving obstacle avoidance in height direction.

Requirements

python == 3.6.2

AirSim == 1.2.0

Keras == 2.1.6

msgpack-rpc-python 0.4.1

numpy == 1.16.0

tensorflow == 1.8.0

pillow == 8.4.0

opencv-python == 3.2.0.7

Framework

Actor-Network

• batch size is 32
• Input of the image branch is previous 4 moments and current 64x64 depth images
• Input of the state branch is previous 4 moments and current height states
• Output of the actor network is current height control instruction

Critic-Network

• batch size is 32
• Input of the image branch is previous 4 moments and current 64x64 depth images
• Input of the state branch is previous 4 moments and current height states
• Input of the action branch is previous 4 moments and current actions
• Output of the critic network is Q value

how to use it

1. run AirSim to start the simulation enviroment
2. run main.py to train it
3. run test.py to test it

If you find my work useful for your research, please give me a star!

I just did it for a course design task of Artificial Neural Network and Control, a master degree course, so there are certainly many bugs and I didn't finetune it. If you want to do some research based on it, you could contact me and we could do it together!