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知识，可用于帮助卫星的地面运营商管理下一代太空任务的复杂性。航天器的地面运营商通常必须平衡多个目标，并且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。但是，现有工具使操作员很难在设计卫星遵循的路径时完全了解可能的权衡和奖励。此外，使用自主权在所需的路径上引导卫星可以带来进一步的复杂性和不确定性。该项目支持以这个问题为动机的研究：在不确定环境中运行的自主系统的路径规划如何对人类，动态和适当的风险水平响应？创建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汽车的复杂任务产生更大的影响。该赠款支持算法的发展和理论方法，以使人类操作员能够无缝地操纵宣教目标，并在策略中无缝地操纵人类操作员，并自动策划自动行动，并自动地策划自动行动。研究方法的前提是，凸优化提供了一个理论框架，不仅为随机运动计划和控制提供了一个理论框架，而且还为对任务参数的风险，奖励和约束的敏感性分析，在很大程度上是由于其以运行时兼容的方式提供证书的能力。 PI的重点是开发系统的方法和工具，用于1）任务目标和约束的规范，而无需专业知识； 2）用户与车辆自主控制系统之间的奖励参数，风险公差和约束的谈判； 3）将这些功能集成到一个退缩的地平线框架中，以使对任务优先事项和操作员偏好的意外和动态变化的响应能力。这项研究的新颖性在于将不确定性的表征包括在随机最佳控制框架中。在使用二元理论来敏感性分析目标，风险和奖励；在运行时实施随机可达性和优化算法在退缩的地平线框架内，以实现实时操作员的支持。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。