NRI: Information-Theoretic Trajectory Optimization for Motion Planning and Control with Applications to Space Proximity Operations

NRI：运动规划和控制的信息理论轨迹优化及其在空间邻近操作中的应用

• 批准号: 1426945
• 负责人: Panagiotis Tsiotras
• 金额: $ 70万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426945&HistoricalAwards=false
• 关键词: NRI; Information; Theoretic; Trajectory; Optimization

Robotic operations in space are indispensable for many missions both in Earth orbit and beyond. Satellite servicing and refueling, space station resupply with consumables, removal of space debris, spacecraft structural integrity inspection, crew assistance, as well as support for deep space missions to Mars and other planets and comets, all require the assistance of highly accurate, reliable and autonomous (or semi-autonomous) space robots. To date, most robotic operations in space are performed in a closely supervised mode by a human operator. This limits both the flexibility and the type of missions that can be performed (for example, the time for light to travel to and from Mars takes about 15 minutes, making "real-time" remote control impossible). This research aims at developing the necessary theory and algorithms to be able to utilize active exploration using robust, reliable sensing and planning of a free-flying space robots in the vicinity of another body, in order to perform proximity operations (including autonomous rendezvous and docking in space). One of the challenges in these types of problems is the uncertainty in understanding the surroundings in order to plan suitable control actions. In order to handle these challenges we utilize novel tools and methodologies from the field of stochastic optimal control along with new advances describing the spacecraft attitude dynamics and kinematics of spacecraft in orbit. In order to ensure that the algorithms we develop perform in real-life as expected, the theoretical results will be experimentally validated on a high-fidelity 5-dof spacecraft simulator facility. This work will have an immediate impact on the US capabilities to perform monitoring and servicing of satellites in space routinely, by advancing the state-of-the-art in perception and path-planning of orbiting spacecraft in the vicinity of another body, man-made or natural. Although the emphasis of this work is primarily on space robotic applications, the same techniques can be used in all similar problems where an intelligent agent needs to navigate autonomously in an uncertain and dynamic environment.The proposed research tackles a fundamental problem in autonomous/robotic systems, namely, the integrated sensing and planning under uncertainty. The current paradigm in the literature utilizes perceptual cues (especially those based solely on visual information) essentially as surrogates of full-state feedback estimators, thus enforcing an artificial separation of perception and control action. This dichotomy between sensory data acquisition/processing, and control/actuation strategies - deeply rooted in the community from its wide applicability to the stabilization of linear systems subject to additive noise (?separation principle?) - is unsuitable for this problem, where information gathering (perception/sensing) is tightly coupled with motion (control). To overcome the aforementioned limitations, in this work it is proposed to use tools from stochastic optimal control in order to extract actionable information from raw sensory inputs. A key ingredient of the proposed approach is to keep track of the first and second order statistics of the estimation error and treat them as the state, so that control actions depend on both of them. The result is a new, computationally more efficient, methodology to maximize information gathering during the exploration phase and to optimize over distributions of trajectories during the execution phase.

太空机器人操作对于地球轨道内外的许多任务都是不可或缺的。 卫星维修和加油、空间站补给消耗品、清除空间碎片、航天器结构完整性检查、乘员协助，以及支持火星等行星和彗星的深空任务，都需要高精度、可靠和可靠的技术协助。自主（或半自主）太空机器人。迄今为止，大多数机器人在太空中的操作都是在人类操作员的严密监督下进行的。这限制了灵活性和可以执行的任务类型（例如，光往返火星的时间大约需要 15 分钟，使得“实时”远程控制变得不可能）。这项研究旨在开发必要的理论和算法，以便能够利用强大、可靠的传感和规划在另一个物体附近自由飞行的空间机器人进行主动探索，以便执行接近操作（包括自主交会和对接）在太空中）。此类问题的挑战之一是了解周围环境以规划适当的控制行动的不确定性。为了应对这些挑战，我们利用随机最优控制领域的新颖工具和方法，以及描述航天器姿态动力学和在轨航天器运动学的新进展。 为了确保我们开发的算法在现实生活中按预期执行，理论结果将在高保真五自由度航天器模拟器设施上进行实验验证。这项工作将通过推进在另一个物体（人类）附近的轨道航天器的感知和路径规划方面的最先进技术，对美国对太空卫星进行例行监测和服务的能力产生直接影响。制作的或天然的。尽管这项工作的重点主要是空间机器人应用，但相同的技术可以用于智能代理需要在不确定和动态环境中自主导航的所有类似问题。所提出的研究解决了自主/机器人系统中的一个基本问题，即不确定性下的综合感知与规划。文献中当前的范式本质上利用感知线索（尤其是那些仅基于视觉信息的线索）作为全状态反馈估计器的替代，从而强制感知和控制动作的人为分离。传感数据采集/处理和控制/驱动策略之间的这种二分法 - 从其广泛适用性到受加性噪声影响的线性系统的稳定性（？分离原理？）深深植根于社区 - 不适合这个问题，其中信息收集（感知/传感）与运动（控制）紧密结合。为了克服上述限制，在这项工作中，建议使用随机最优控制工具，以便从原始感官输入中提取可操作的信息。该方法的一个关键要素是跟踪估计误差的一阶和二阶统计数据并将它们视为状态，以便控制动作取决于它们两者。其结果是一种新的、计算效率更高的方法，可以在探索阶段最大限度地收集信息，并在执行阶段优化轨迹分布。