多智能体在轨巡检任务的相关导航方法

Mark Mercier, D. Curtis
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引用次数: 0

摘要

在轨检查往往是维修和清除碎片等卫星作业的必要先决条件。特别是,多个检查员协作检查未知对象可以导致更快、更全面的检查。本研究的贡献是在多智能体检测中为每个智能体相对于目标的位置提供一个可靠的、计算效率高的估计。首先,概述了问题动态、背景和用于构建和解决基于因子图的估计技术的方法。通过滑动窗口(SW)过滤器的实现,因子图可以应用于长持续时间的场景,同时减少对估计的处理需求。一个示例场景,三个代理试图进行检查,同时易受太阳隔离区的影响,使一些测量信息无效。通过将开发的滑动窗口因子图(SWFG)的结果与两种极端的SW大小进行比较,开发继续进行:迭代卡尔曼滤波器(SW大小= 1)和全因子图(SW大小≥场景持续时间)。结果揭示了太阳阻隔区和滑动窗口大小对估计质量和计算时间的影响。
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Relative Navigation Methods for a Multi-Agent, On-Orbit Inspection Mission
On-orbit inspection is often a necessary prerequisite to satellite operations such as servicing and debris removal. In particular, multiple inspectors collaborating to inspect an unknown object can result in a faster and more comprehensive inspection. The contribution of this research is to provide a reliable, computationally efficient estimate for the position of each agent relative to a target in a multi-agent inspection. First, an overview of the problem dynamics, background, and methods used to build and solve a factor graph-based estimation technique is provided. Through the implementation of a Sliding Window (SW) filter, a factor graph can be applied to long duration scenarios while reducing processing requirements for estimation. An example scenario of three agents attempting to conduct an inspection while susceptible to a Sun exclusion zone that nulls some measurement information is prescribed. Development continues by comparing results for the developed Sliding Window Factor Graph (SWFG) against two extreme SW sizes of interest: an iterative Kalman filter (SW size = 1) and a full factor graph (SW size ≥ scenario duration). Results reveal the impact of both the Sun exclusion zone and sliding window size on estimation quality and computation time.
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