SNCOAT

Space Non-Cooperative Object Active Tracking, which means the spacecraft approach to an unknown target only with vision camera. We propose an end-to-end active visual tracking method based on deep Q-learning algorithm, named as DRLAVT. It can guide the chasing spacecraft approach to arbitrary space non-cooperative target merely relied on color or RGBD images, which significantly outperforms PBVS method.

See more details about SNCOAT benchmark in Space Non-Cooperative Object Active Tracking With Deep Reinforcement Learning.

If you use our benchmark or related codes, please cite:

@article{zhou2022space,
  title={Space Non-cooperative Object Active Tracking with Deep Reinforcement Learning},
  author={Zhou, Dong and Sun, Guanghui and Lei, Wenxiao and Wu, Ligang},
  journal={IEEE Transactions on Aerospace and Electronic Systems},
  year={2022},
  publisher={IEEE}
}

MORE SOURCE CODES ARE COMING SOON ...

Requirement

• PyTorch ~= 1.6.0
• CoppeliaSim v4.2
• PyRep
• gym
• matplotlib

[Note]: This Program only validated on Ubuntu16.04 and Centos7 platform.

Simulated Env

Scenes

Download scenes at first: Googel Drive | Baidu NetDisk(code:1111)

We construct 18 scenes with different types of space non-cooperative object, including asteroids, capsules, rockets, satellites, and stations. $\frac{2}{3}$ targets are used for training, the others for evaluation.

• SNCOAT-Asteroid-(v0-v5)
• SNCOAT-Capsule-(v0-v2)
• SNCOAT-Rocket-(v0-v2)
• SNCOAT-Satellite-(v0-v2)
• SNCOAT-Station-(v0-v2)

Try Simulated Env

Get start with Simulated Env

PBVS Baseline Algorithm

The list of Trackers have been validated in our PBVS framework:

• SiamRPN
• SiamFC
• KCF

Run PBVS based on KCF Tracker

Run PBVS based on SiamRPN Tracker

DRLAVT Algorithm

Evaluate DRLAVT with Pretrained Model

Train DRLAVT from scratch