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Collaborative Research: Negotiated Planning for Stochastic Control of Dynamical Systems

协作研究：动力系统随机控制的协商规划

基本信息

批准号：
2105502
负责人：
Behcet Acikmese
金额：
$ 40.78万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105502&HistoricalAwards=false
关键词：
Collaborative Research Negotiated Planning Stochastic

项目摘要

This project focuses on the development of new computational tools and new knowledge that can be used to help ground operators of satellites manage the complexity of next generation space missions. Ground operators of spacecraft typically must balance multiple, conflicting goals, and as spacecraft missions become more complex, so will the ground operator's task of satellite coordination. However, existing tools make it difficult for operators to obtain a complete understanding of possible trade-offs and rewards when designing paths for the satellites to follow. Further, the use of autonomy to guide satellites along desired paths can introduce further complexity, as well as uncertainty. This project supports research that is motivated by the question: How can path planning for autonomous systems operating in uncertain environments, be responsive to the human, the dynamics, and appropriate levels of risk? Creation of a mathematical and algorithmic framework to accomplish these objectives could have broader impact on complex missions involving autonomous vehicles in other domains beyond spacecraft.This grant supports the development of algorithms and theoretical methods to enable the human operator to seamlessly manipulate mission objectives, risks, and rewards in path planning for controlled autonomous vehicles. The research approach is premised on the notion that convex optimization provides a theoretical framework for not only stochastic motion planning and control, but also for sensitivity analysis of the risks, rewards, and constraints, to mission parameters, in large part due to its ability to provide certificates in a run-time compatible manner. The PIs focus on the development of systematic methods and tools for 1) specification of mission objectives and constraints without the need for expert knowledge; 2) negotiation of reward parameters, risk tolerances, and constraints, between the user and the vehicle's autonomous control system; and 3) integration of these capabilities into a receding horizon framework, to enable responsiveness to unanticipated and dynamic changes to mission priorities and operator preferences. The novelty of this research is in the inclusion of data driven characterization of uncertainty into a stochastic optimal control framework; in the use of duality theory for sensitivity analysis of objectives, risks, and rewards; and in the run-time implementation of stochastic reachability and optimization algorithms within a receding horizon framework, to enable real-time operator support.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目重点开发新的计算工具和新知识，可用于帮助卫星地面操作员管理下一代太空任务的复杂性。航天器的地面操作员通常必须平衡多个相互冲突的目标，并且随着航天器的任务变得更加复杂，地面操作员的卫星协调任务也将变得更加复杂。然而，现有的工具使得运营商在设计卫星遵循的路径时很难完全了解可能的权衡和回报。此外，使用自主性来引导卫星沿所需路径行驶可能会带来进一步的复杂性和不确定性。该项目支持由以下问题引发的研究：在不确定环境中运行的自主系统的路径规划如何响应人类、动态和适当的风险水平？创建数学和算法框架来实现这些目标可能会对涉及航天器以外其他领域的自动驾驶车辆的复杂任务产生更广泛的影响。这笔赠款支持算法和理论方法的开发，使人类操作员能够在受控自动驾驶车辆的路径规划中无缝操纵任务目标、风险和奖励。该研究方法的前提是凸优化不仅为随机运动规划和控制提供了理论框架，而且还为任务参数的风险、回报和约束的敏感性分析提供了理论框架，这在很大程度上是因为它能够以运行时兼容的方式提供证书。 PI 专注于开发系统方法和工具，以实现以下目的：1）无需专业知识即可明确任务目标和约束条件； 2）用户和车辆自主控制系统之间奖励参数、风险承受能力和约束的协商； 3）将这些能力集成到后退框架中，以便能够响应任务优先级和操作员偏好的意外动态变化。这项研究的新颖之处在于将数据驱动的不确定性特征纳入随机最优控制框架中；使用二元理论对目标、风险和回报进行敏感性分析；在后退框架内运行时实现随机可达性和优化算法，以实现实时操作员支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。