RI: Small: Plan Execution for Continuous Dynamical Systems Within Risk Bounds

RI：小型：风险范围内连续动态系统的计划执行

• 批准号: 1017992
• 负责人: Brian Williams
• 金额: $ 29.88万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017992&HistoricalAwards=false
• 关键词: RI; Small; Plan; Execution; Continuous

In many applications, from undersea to space, an autonomous agent is given a set of operational goals to achieve optimally, while taking into account the uncertainties that arise from uncontrollable events. For example, in a monitoring mission for an autonomous under-water vehicle (AUV), the goal might be to maximize scientific return, while avoiding hazards. Due to uncertainty, it is unrealistic for many real-world mission to guarantee 100% success. One approach explored extensively within the decision-theoretic planning community is to maximize an objective that trades risk for utility. However, this does not provide any hard guarantees. An alternative approach, commonly employed in engineering practice is to specify risk as a hard constraint, in terms of an upper bound on mission failure (called a chance constraint). For example, NASA Mars missions are designed to meet or exceed a requirement on the probability of successful landing; human-rated vehicles are designed to similar requirements. Given a chance-constrained mission, an agent performing the mission may strive to maximize expected reward, while ensuring that the chance constraint and other operating constraints are met. This research is developing a model-based executive that achieves goal-level plans within specified risk bounds, while attempting to maximize expected reward. Key attributes of this executive include: 1) user specification of time-evolved goal behaviors; 2) plan execution by generating a sequence of discrete and continuous actions for controlling the plant; and 3) optimal, stochastic planning of control actions within risk bounds and dynamic constraints. The research under this grant is laying the groundwork for longer-term objectives of facilitating continuous adaptation of the initial plan and allocation of risk as uncertainties are resolved during plan execution. Among other applications, we plan tests with AUVs engaged in scientific missions, measuring performance within obstacle-related, time and energy bounds.

在许多应用程序中，从海底到太空，都为自治代理提供了一组操作目标，以实现最佳实现，同时考虑到无法控制的事件引起的不确定性。例如，在监视自动驾驶汽车（AUV）的监视任务中，目标可能是最大程度地提高科学回报，同时避免危害。由于不确定性，许多现实世界任务保证100％成功是不现实的。在决策理论计划社区中广泛探索的一种方法是最大化一个目标来交易效用风险。但是，这并不能提供任何难道。工程实践中通常采用的一种替代方法是将风险指定为严格的约束，以任务失败的上限（称为机会约束）。例如，NASA火星任务旨在满足或超出成功着陆的可能性的要求；人级的车辆被设计为类似的要求。鉴于偶然的任务，执行任务的代理商可能会努力最大程度地提高预期奖励，同时确保满足机会限制和其他操作约束。 这项研究正在开发一个基于模型的高管，该高管在指定的风险范围内实现目标级别的计划，同时试图最大程度地提高预期奖励。该高管的关键属性包括：1）时间进化目标行为的用户规范； 2）通过生成一系列控制植物的离散和连续动作来计划执行； 3）在风险范围和动态限制内，控制动作的最佳，随机计划。该赠款下的研究为长期目标奠定了基础，以促进最初计划和风险分配，因为在计划执行过程中解决了不确定性。在其他应用中，我们计划使用从事科学任务的AUV进行测试，测量与障碍有关的时间和能量界限内的性能。