Autonomous collision avoidance sample grasping method for extraterrestrial exploration. (April 2022)

• Record Type: Journal Article
• Authors: Huang, Huang; Xie, Xinru; Tang, Liang; Liu, Hao; Liu, Nailong; Li, Mou
• Is Part Of: Acta astronautica. Volume 193(2022)
• Page Start: 303
• Page End: 310
• Publication Date: 2022-04
• Subjects: Robotic grasping -- Extraterrestrial exploration -- Deep reinforcement learning -- Collision avoidance; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2022.01.017
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: Collecting samples from extraterrestrial body is of great significance for deep space scientific exploration. This paper proposes an autonomous grasping method to collect unknown natural samples from unstructured extraterrestrial surface in a safe, gentle and robust way. A deep reinforcement learning based end-to-end grasping pose estimation framework is designed. The proposed framework takes visual information as input and learns an appropriate grasping strategy. A feature extraction deep neural network and a reinforcement learning policy network are trained simultaneously so as to obtain light-weight networks for extraterrestrial detector. Meanwhile, in order to protect the sample from being damaged during grasping, collision detection is incorporated into the closed-loop during training. The grasping strategy is trained in simulation and is then transferred to real-world. Simulation and real-world experiments show that the learned policy can adapt to unseen irregular stones with less times of collision and at a high grasp success rate under single, scattered, and cluttered scenes. Highlights: Autonomous grasping method is proposed to collect unknown natural samples. Deep reinforcement learning based end-to-end grasping pose estimation framework is designed.. Light-weight networks is built for extraterrestrial detector. Gentle grasping behavior is achieved for safety and reliability concern. The learned policy is verified by simulation and real-world experiments.