火星2020火星车飞行软件词典开发

M. Muszynski, E. Fosse, Andrew Plave, G. Pyrzak
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引用次数: 1

摘要

由喷气推进实验室(JPL)开发和运营的“火星2020”项目于2021年2月18日成功将“毅力”号火星车及其飞行伙伴“独创性”号降落在火星表面。毅力号结合了传统和尖端的飞行软件和硬件,以完成与火星表面采样相关的关键任务要求。NASA大型战略科学任务的设计、开发和运行需要将航天器的能力传达给跨多个学科的数百名工程师。飞行和地面软件开发、验证和确认(V&V)、装配、测试和发射操作(ATLO)以及管理之间的相互作用都需要快速理解每个学科的独特信息片段。这些信息包括飞行系统的当前能力以及未来能力及其在开发和测试中的状态。尽管这些信息的基础和关键性质,用于跟踪它的飞行软件字典是许多项目的绊脚石。这些词典为解释从航天器发送的数据提供了基础,使地面上的工程师能够快速理解。在航天器开发和运行期间,由于大量的接口系统和每个系统微妙而独特的需求,飞行软件字典管理包括重大挑战。Mars2020的飞行软件字典工程面临许多挑战,最值得注意的是:并行字典开发,以支持每个任务阶段(巡航和地面)同时独立的飞行软件构建活动,管理操作支持信息的请求,而不会干扰与火星车的遗产接口,字典利益相关者引入新工具,迫使字典团队创新和重新设计遗产工具链。这些挑战产生了字典开发工作的指导原则:强调字典代码开发工具链中的编码最佳实践和单元测试,尽可能使用机构提供的COTS(商业现货)工具,并在通过松耦合接口推进操作支持信息的同时维护传统的飞行-地面接口。在整个开发和运营过程中,Mars2020字典工具链包括IBM DOORS Next Generation、GitHub、Microsoft Excel、Docker、Jenkins和一个重要的定制Python代码库。重要的接口包括喷气推进实验室的指挥和控制软件、传统飞行软件团队工具和过程,以及为任务开发的许多基于云的地面工具。本文将讨论Mars2020词典开发的需求、开发团队对这些需求的响应、整个过程中的经验教训、为实现自动化交付和利益相关者输入的持续集成而采取的步骤、Mars2020潜在的工具链改进以及可应用于未来任务的关键要点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flight Software Dictionary Development for the Mars2020 Rover
The Mars2020 project, developed and operated by the Jet Propulsion Laboratory (JPL), successfully landed the Perseverance rover and its flying companion Ingenuity on the surface of Mars on February 18th 2021. Perseverance com-bines heritage and cutting-edge flight software and hardware to accomplish crucial mission requirements related to Martian surface sampling. The design, development, and operation of NASA's large strate-gic science missions require the ability to communicate space-craft capabilities to hundreds of engineers across multiple dis-ciplines. The interactions between flight and ground software development, Verification and Validation (V&V), Assembly, Test, and Launch Operations (ATLO), and management each demand quick understanding of unique slices of information for each discipline. This information includes the current capabil-ities of the flight system as well as future capabilities and their status as they are developed and tested. Despite the fundamental and critical nature of this information, the flight software dictionaries used to track it are a stumbling block for many projects. These dictionaries provide the cor-nerstone for the interpretation of data sent from the spacecraft, allowing for quick comprehension by engineers on the ground. During both spacecraft development and operations, flight soft-ware dictionary management includes significant challenges due to the large number of interfacing systems and the subtle yet distinct needs of each. The engineering of flight software dictionaries for Mars2020 had numerous challenges, most-notably: parallel dictionary development to support simultaneous separate flight software build campaigns for each mission phase (cruise and surface), managing requests for operations-enabling information without perturbing the heritage interface with the rover, and the intro-duction of new tools by the dictionary stakeholders that forced the dictionary team to innovate and redesign the heritage tool chain. These challenges generated guiding principles for the dictionary development effort: emphasize coding best practices and unit testing in the dictionary code development tool chain, use institutionally provided COTS (commercial-off-the-shelf) tools whenever possible, and maintain the heritage flight-ground interface all while advancing operations-enabling information via a loosely coupled interface. Throughout development and operations, the Mars2020 dictionary toolchain included IBM DOORS Next Generation, GitHub, Microsoft Excel, Docker, Jenkins, and a significant custom-built Python codebase. Significant interfaces included JPL's command and control software, heritage flight software team tools and processes, and the many cloud-based ground tools developed for the mission. This paper will discuss the requirements for the Mars2020 dictionary development, the development team's response to those requirements, lessons learned throughout the process, steps taken towards automated deliveries and continuous integration of stakeholder inputs, potential toolchain improvements for Mars2020, and key takeaways that could be applied to future missions.
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