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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.

该项目的重点是开发新的计算工具和新的知识，可用于帮助卫星地面运营商管理下一代空间任务的复杂性。航天器的地面操作员通常必须平衡多个相互冲突的目标，随着航天器任务变得更加复杂，地面操作员的卫星协调任务也将变得更加复杂。然而，现有的工具使运营商在设计卫星遵循的路径时难以完全了解可能的权衡和回报。此外，使用自主性来沿着沿着期望路径引导卫星可能引入进一步的复杂性以及不确定性。该项目支持的研究是出于这样的问题：如何可以路径规划的自主系统在不确定的环境中运行，响应人，动态和适当的风险水平？建立一个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。该资助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地操纵使命目标、风险和回报。的研究方法的概念，凸优化提供了一个理论框架，不仅随机运动规划和控制，但也为敏感性分析的风险，回报和约束条件，使命参数，在很大程度上是由于它的能力，以运行时兼容的方式提供证书。PI的重点是系统方法和工具的开发：1）使命目标和约束的规范，而不需要专家知识; 2）用户和车辆自主控制系统之间的奖励参数，风险容忍度和约束的协商;以及3）将这些能力整合到一个后退地平线框架中，从而能够对使命优先级和操作员偏好的意外和动态变化做出响应。本研究的新奇在于将数据驱动的不确定性特征纳入随机最优控制框架，利用对偶理论对目标、风险和回报进行敏感性分析，并且在滚动时域框架内的随机可达性和优化算法的运行时实现中，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。