.png){kind=icon}
返回
Autonomous collision avoidance sample grasping method for extraterrestrial exploration
.png){kind=icon}
PRE
AI
DOI：10.1016/j.actaastro.2022.01.017.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Robotic grasping
Extraterrestrial exploration
Deep reinforcement learning
Collision avoidance
期刊
IF:
3.4
论文数:
1.2W
被引数:
2.1W
机构
暂无机构信息
引用论文
分享收藏Motion control of a space manipulator using fuzzy sliding mode control with reinforcement learning.png)
ACTA ASTRONAUTICA
ACTA ASTRONAUTICAIF3.4
PREAI
分享收藏分享收藏Hayabusa - Its technology and science accomplishment summary and Hayabusa-2ACTA ASTRONAUTICA
ACTA ASTRONAUTICAIF3.4
PREAI
分享收藏AMP: Adversarial Motion Priors for Stylized Physics-Based Character ControlAMP: 基于物理的程式化角色控制的对抗运动先验
OAAI
OAAI分享收藏Drilling load modeling and validation based on the filling rate of auger flute in planetary sampling行星取样中基于螺旋槽填充率的钻井载荷建模与验证OAAI
OAAI分享收藏Path planning for asteroid hopping rovers with pre-trained deep reinforcement learning architecturesACTA ASTRONAUTICA
ACTA ASTRONAUTICAIF3.4
PREAI
分享收藏Autonomous closed-loop guidance using reinforcement learning in a low-thrust, multi-body dynamical environment在低推力，多体动力学环境中使用强化学习的自主闭环制导ACTA ASTRONAUTICAOAAI
ACTA ASTRONAUTICAIF3.4
OAAI分享收藏
查看更多内容