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.

太空中的机器人操作对于地球轨道和地球轨道以外的许多任务都是不可或缺的。 卫星维修和加油、空间站消耗品再补给、清除空间碎片、航天器结构完整性检查、机组人员协助以及对前往火星和其他行星和彗星的深空飞行任务的支持，都需要高度准确、可靠和自主（或半自主）的空间机器人的协助。到目前为止，大多数机器人在太空中的操作都是在人类操作员的密切监督下进行的。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，这使得“实时”远程控制变得不可能）。这项研究的目的是开发必要的理论和算法，以便能够利用主动探索，对另一物体附近的自由飞行空间机器人进行稳健、可靠的感知和规划，以便进行邻近作业（包括空间自主交会和对接）。在这些类型的问题的挑战之一是在了解周围的不确定性，以便计划合适的控制措施。为了应对这些挑战，我们利用新的工具和方法，从随机最优控制领域的沿着新的进展，描述航天器的姿态动力学和航天器在轨道上的运动学。 为了确保我们开发的算法在现实生活中的预期执行，理论结果将在高保真5-DOF航天器模拟器设备上进行实验验证。这项工作将通过推进另一个人造物体或自然物体附近的轨道航天器的感知和路径规划的最新技术，对美国定期对太空卫星进行监测和服务的能力产生直接影响。虽然这项工作的重点主要是在空间机器人的应用，同样的技术可以用于所有类似的问题，其中一个智能代理需要自主导航在一个不确定的和动态的环境中，拟议的研究解决的一个基本问题，在自主/机器人系统，即集成的传感和规划下的不确定性。目前的范式在文献中利用感知线索（特别是那些仅仅基于视觉信息）基本上作为代理人的全状态反馈估计，从而强制执行人工分离的感知和控制动作。这种感觉数据采集/处理和控制/驱动策略之间的二分法-深深植根于社区，从其广泛的适用性，以稳定的线性系统受到加性噪声（？分离原则？）- 不适合这个问题，其中信息收集（感知/传感）与运动（控制）紧密耦合。为了克服上述限制，在这项工作中，它建议使用随机最优控制工具，以提取可操作的信息，从原始的感官输入。所提出的方法的一个关键组成部分是保持跟踪的估计误差的第一和第二阶统计量，并将它们作为状态，使控制动作取决于它们两者。其结果是一个新的，计算更有效的方法，以最大限度地提高信息收集在探索阶段，并在执行阶段的轨迹分布进行优化。