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AstroSLAM - A Robust and Reliable Visual Localization and Pose Estimation Architecture for Space Robots in Orbit

AstroSLAM - 用于轨道空间机器人的稳健可靠的视觉定位和姿态估计架构

基本信息

批准号：
2101250
负责人：
Panagiotis Tsiotras
金额：
$ 76.09万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2101250&HistoricalAwards=false
关键词：
AstroSLAM Robust Reliable Visual Localization

项目摘要

Space robotics is essential for all current and future space exploration and utilization missions. Advanced space-robotic technologies will enable in-orbit servicing and refueling of satellites, and will support more elaborate missions such as in-orbit large flexible structure assembly, debris removal, inspection, hardware upgrades, etc. For instance, current communication satellites have a typical lifetime of 10 to 15 years, at which point an otherwise perfectly functioning satellite runs out of propellant and is decommissioned. Refueling the satellite can add several years of additional lifetime and revenue. Future envisioned missions to the Moon, Mars and beyond will also require advanced robotic capabilities in orbit. Many future exploration missions envision integrated teams of astronauts and free flying “co-robots” that support or monitor the human crew activities. What is currently missing from these missions is the ability to provide full 4D (space-time) situational awareness using autonomous on-board perception and planning capabilities. Recent technological breakthroughs for ground robots pave the way for similar advancements in robotic in-orbit operations to enable routine robotic operations in space in the not-so-distant future. These include perception and planning algorithms, machine learning based pattern recognition, autonomy, new computer hardware architectures, human-machine interfaces, and dexterous manipulation, among many others. The outcome of the research will be the ability of astronauts and space robots to work together to enable inspection, monitoring, and classification of resident space objects; maneuvering and proximity operations and docking; salvage and retrieval of malfunctioning or tumbling spacecraft; and servicing, construction, repair, upgrade, and refueling missions of assets in orbit.This project will develop novel visual perception, localization, mapping, and planning algorithms that will enable new capabilities in terms of situational awareness for space robots that can work alone or alongside astronauts in orbit. The research plan includes the development of novel automated feature extraction and matching algorithms using deep neural network architectures, adapted to the challenging imaging conditions (collimated light, high contrast, lack of atmospheric scattering, paucity of distinctive features, etc.) and orbital motion constraints imposed in space. This will enable robust and reliable relative pose estimation, 3D shape reconstruction and characterization of space objects. Novel optimal planning and prediction methods based on a factor-graph optimization framework will be matched to these new perception capabilities to account for fuel usage and orbital motion constraints. The experimental validation of the theory will take place at the Georgia Tech Autonomous Spacecraft Testing of Robotic Operations in Space platform, a state-of-the-art spacecraft simulation platform. The research will involve both graduate and undergraduate students. The results of this research will be disseminated to the community by journal and conference publications, organization of invited workshops and seminar presentations, and by targeted exposure (press releases, interviews) to popular media.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

太空机器人对于当前和未来的所有太空探索和利用任务至关重要。先进的空间机器人技术将使卫星在轨维修和加油成为可能，并支持更复杂的任务，如在轨大型柔性结构组装、碎片清除、检查、硬件升级等。例如，当前通信卫星的典型寿命为10至15年，此时，原本功能良好的卫星将耗尽推进剂并退役。为卫星补充燃料可以增加数年的使用寿命和收入。未来设想的月球、火星及更远距离的任务也将需要先进的轨道机器人能力。许多未来的探索任务都设想由宇航员和自由飞行的“协作机器人”组成的综合团队来支持或监控人类机组人员的活动。目前这些任务缺少的是利用自主机载感知和规划功能提供完整 4D（时空）态势感知的能力。地面机器人最近的技术突破为机器人在轨操作的类似进步铺平了道路，从而在不远的将来实现太空中的常规机器人操作。其中包括感知和规划算法、基于机器学习的模式识别、自主性、新的计算机硬件架构、人机界面和灵巧操作等。该研究的成果将是宇航员和太空机器人能够协同工作，对驻留空间物体进行检查、监测和分类；机动和邻近操作以及对接；打捞和回收发生故障或翻滚的航天器；该项目将开发新颖的视觉感知、定位、测绘和规划算法，为能够在轨道上单独工作或与宇航员一起工作的太空机器人提供态势感知方面的新功能。该研究计划包括使用深度神经网络架构开发新颖的自动特征提取和匹配算法，适应具有挑战性的成像条件（准直光、高对比度、缺乏大气散射、缺乏独特特征等）和空间中施加的轨道运动约束。这将实现稳健可靠的相对位姿估计、3D 形状重建和空间物体的表征。基于因子图优化框架的新颖的优化规划和预测方法将与这些新的感知能力相匹配，以考虑燃料使用和轨道运动约束。该理论的实验验证将在佐治亚理工学院的空间机器人操作自主航天器测试平台进行，该平台是最先进的航天器模拟平台。该研究将涉及研究生和本科生。这项研究的结果将通过期刊和会议出版物、组织受邀研讨会和研讨会演示以及通过大众媒体的有针对性的曝光（新闻稿、采访）向社会传播。该项目得到了机器人学跨部门基础研究计划的支持，该计划由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (CISE) 共同管理和资助。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。