摘要
围绕地外探测任务对全自主操控的需求,阐述了智能技术的引入对地外探测操控的重要意义。根据地外探测操控任务的发展现状和特点,总结出地外探测自主操控面临的挑战与难点,对现有基于深度强化学习的操控算法进行概括。以地外探测自主操控任务难点为驱动,对深度强化学习(DRL)技术在地外探测操控中的应用及成果进行了综述与分析,概括了未来地外探测自主智能操控发展中涉及的关键技术问题。
According to the higher requirements with regard to control system autonomy for future celestial body exploration missions,the importance of intelligent control technology is introduced.Based on the characteristics of manipulation missions for celestial bodies exploration,the technical challenges of autonomous control are analyzed and summarized.Existing Deep Reinforcement Learning(DRL)based autonomous manipulation algorithms are summarized.According to different difficulties faced by the deep learning based manipulation missions for celestial bodies,achievements of applications of the manipulation skills based on DRL methods are discussed.A prospect of future research directions for intelligent manipulation technologies is given.
作者
高锡珍
汤亮
黄煌
Xizhen GAO;Liang TANG;Huang HUANG(Beijing Institute of Control Engineering,Beijing 100094,China;Key Laboratory of Space Intelligent Control Technology,Beijing 100094,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2023年第6期35-49,共15页
Acta Aeronautica et Astronautica Sinica
基金
国家重点研发计划(2018AAA0102700)。
关键词
地外探测
深度强化学习
自主操控
着陆巡视
采样
celestial bodies exploration
deep reinforcement learning
autonomous manipulation
landing and roving exploration
sample acquisition
