Robust Active Visual Tracking of Space Non-Cooperative Objects
- Resource Type
- Conference
- Authors
- Shao, Shibo; Zhou, Dong; Peng, Xiaoxu; Hu, Yuhui; Sun, Guanghui
- Source
- 2023 China Automation Congress (CAC) Automation Congress (CAC), 2023 China. :2561-2566 Nov, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Resistance
Employee welfare
Visualization
Target tracking
Perturbation methods
Robustness
Active Visual Tracking
Robust Deep Reinforcement Learning
Action Command Failure
Deep Deterministic Policy Gradient (DDPG)
- Language
- ISSN
- 2688-0938
Deep Reinforcement Learning (DRL) based Active Visual Tracking (AVT) algorithms targeting Space Non-cooperative Objects (SNCOs) is very vulnerable to various perturbations such as temporal action control command failure, actuator failure or signal transmission failure. Such perturbations can severely affect the performance of active visual trackers. Thus in this paper, targeting action failure, a robust DDPG based AVT algorithm is proposed which uses a new reward function to prevent DRL over-fitting. The proposed algorithm shows resistance to the perturbation and is able to perform outstanding tracking under high action failure probability. Sufficient experiments were conducted to verify the effectiveness and advancement of the proposed algorithm.