Abstract

Target capture is the basis of many space missions. With free-floating space manipulators(FFSM) are more and more widely used in various space tasks, using FFSM to catch space targets has become a research hotspot. In this research, we propose an end-to-end capture strategy under unknown dynamics based on deep reinforcement learning, which directly controls the manipulator with the raw camera image as the input. The algorithm take imitation learning for pre-training. The policy after pre-training process will explore high-reward regions in the state space with higher probability. This will improve the efficiency of DRL exploring in the high-dimensional state space composed of images. We built a simulation environment in CoppeliaSim to train and test our algorithm. The results show that our algorithm has achieved good results in end-to-end target capture task without dynamic modeling and is more efficient than pure model free reinforcement learning.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call