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.

在从海底到太空的许多应用中，自治智能体被赋予了一套操作目标，以最优方式实现，同时考虑到不可控事件引起的不确定性。例如，在自动水下航行器(AUV)的监测任务中，目标可能是最大化科学回报，同时避免危险。由于不确定性，对于许多现实世界的任务来说，保证100%成功是不现实的。在决策理论规划界广泛探索的一种方法是最大化一个以风险换取效用的目标。然而，这并没有提供任何硬保证。工程实践中常用的另一种方法是将风险指定为硬约束，即任务失败的上限(称为机会约束)。例如，NASA的火星任务被设计成满足或超过成功着陆概率的要求；人类额定的飞行器也设计成类似的要求。给定一个机会受限的任务，执行任务的代理可能会努力最大化预期回报，同时确保机会约束和其他操作约束得到满足。这项研究正在开发一种基于模型的高管，在特定的风险范围内实现目标水平的计划，同时试图最大化预期回报。该执行器的关键属性包括：1)用户指定随时间演变的目标行为；2)通过生成用于控制对象的离散和连续动作序列来执行计划；以及3)在风险界限和动态约束内对控制动作进行最优、随机规划。这笔赠款下的研究正在为促进初始计划的持续适应和风险分配的长期目标奠定基础，因为不确定因素在计划执行期间得到解决。在其他应用中，我们计划对执行科学任务的AUV进行测试，测量与障碍相关的、时间和精力范围内的性能。