多任务自主交会对接及相关导航能力研究进展

K. Miller, J. Masciarelli, R. Rohrschneider
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引用次数: 6

摘要

相对导航(RelNav)应用已被确定为许多NASA未来任务架构的使能功能,包括交会、近距离操作和对接(RPOD),以及行星表面交会和着陆、表面机动和空间服务。功能能力已经维持和使用了几十年,但最近的设计和测试工作已经验证了主动和被动相对导航传感器和处理的功能和性能水平的革命性改进,为先进的自主操作提供了途径。这些进步是多学科的,包括紧凑的多任务算法设计,高性能相机技术,闪光激光雷达的进步,空间光调制与系统工程。许多关键技术已经在机载测试项目中得到了验证,一些关键的闪光激光雷达进展在猎户座RelNav风险缓解(STORRM)相关导航传感器套件的传感器测试中获得了飞行资格,该测试于2011年在STS-134上进行。STORRM传感器套件旨在为人类和机器人任务提供高度可靠、紧凑、轻便的解决方案。本文提供了通过STORRM进行相对导航的相关进展和性能结果,以及其他RelNav技术的进步和与未来任务应用的相关性。研究了具有自然目标和人为目标、特征集以及不同程度自治的协作和非协作任务应用环境。
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Advances in multi-mission autonomous rendezvous and docking and relative navigation capabilities
Relative Navigation (RelNav) applications, have been identified as an enabling function of many of NASA's future mission architectures, including Rendezvous, Proximity Operations and Docking (RPOD), as well as planetary surface rendezvous and landing, surface mobility and in space servicing. Functional capabilities have been maintained and exercised for decades, but recent design and test efforts have validated revolutionary improvements in functionality and performance levels of active and passive relative navigation sensors and processing, providing a pathway for advanced autonomous operations The advances are multidisciplinary and include compact, multimission algorithm design, high performance camera technology, flash LIDAR advances, spatial light modulation and systems engineering. Many of the key technologies have been demonstrated in airborne test programs, and some of the key flash LIDAR advances were flight qualified on the Sensor Test for Orion RelNav Risk Mitigation (STORRM) relative navigation sensor suite, flown in 2011 on STS-134. The STORRM sensor suite has been developed to provide a highly reliable, compact, lightweight solution for human and robotic missions. This paper provides progress and performance results associated with relative navigation via STORRM, as well as other RelNav technology advancement and relevance to future mission applications. Cooperative and noncooperative mission application environments with natural and manmade targets and feature sets and varying degrees of autonomy are addressed.
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