On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

中文翻译：

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ReSWARM 微重力飞行实验：在轨近距离运行的规划、控制和模型估计

在轨近距离操作涉及机器人航天器的操纵和为越来越多需要自主的任务场景做出决策，包括在轨组装、维修和宇航员援助。在这些场景中，在轨组装是一种使能技术，可以使用较小的构建块模块在原位建造大型空间结构。然而，与许多此类场景一样，机器人在轨组装涉及多个技术障碍，例如更改系统模型。例如，由自由飞行的“组装机”机器人移动的抓斗模块可能会导致组合系统惯性发生显着变化，这会对自主堆栈的运动规划和控制部分产生连锁影响。此外，在轨装配和其他场景需要避免碰撞的运动规划，特别是在多个装配机器人和结构的“施工现场”场景中操作时。这项工作详细介绍了解决自主微重力近距离操作这些复杂因素的多项关键技术，特别是：（1）全球长期规划的应用，使用考虑系统动力学的基于离线和在线采样的规划器选项来完成； (2) 采用最近提出的 RATTLE 信息感知规划框架来进行在轨重配置模型学习； (3) 与鲁棒控制工具连接，利用当前系统知识提供低级控制鲁棒性。这些方法通过在国际空间站上使用 NASA 的 Astrobee 机器人进行的相对卫星加温和机器人操纵 (ReSWARM) 实验在自主在轨组装用例中进行了演示。提供了 ReSWARM 实验的结果以及重要的操作和实施细节，讨论了硬件实施的实用性以及在微重力下使用 Astrobee 自由飞行机器人的独特方面。 ReSWARM 为机器人近距离操作提供了一套基本的规划和控制工具，在微重力下进行了演示，并概述了未来自主自由飞行器需要考虑的一些重要硬件方面。