Collaborative Research: Negotiated Planning for Stochastic Control of Dynamical Systems

该项目的重点是开发新的计算工具和新知识，以帮助卫星地面操作员管理下一代空间飞行任务的复杂性。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跟踪路径时，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着所需的路径运行，可能会带来进一步的复杂性和不确定性。该项目支持以下问题的研究：在不确定的环境中运行的自主系统的路径规划如何对人、动态和适当水平的风险做出响应？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车辆的复杂任务产生更广泛的影响。这笔赠款支持开发算法和理论方法，使人类操作员能够无缝地操纵受控自动驾驶车辆的路径规划中的任务目标、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。绩效指标侧重于开发系统化的方法和工具，用于1)在不需要专业知识的情况下指定任务目标和约束；2)在用户和车辆的自动控制系统之间协商奖励参数、风险容忍度和约束；3)将这些能力集成到滚动范围框架中，以实现对任务优先级和操作员偏好的意外和动态变化的响应。这项研究的创新之处在于将不确定性的数据驱动表征纳入随机最优控制框架；使用二元性理论对目标、风险和回报进行敏感性分析；以及在滚动范围框架内运行时实施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势和更广泛的影响审查标准进行评估，被认为值得支持。

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.