CPS: Medium: Information based Control of Cyber-Physical Systems operating in uncertain environments

CPS：中：在不确定环境中运行的信息物理系统的基于信息的控制

基本信息

批准号：
1837515
负责人：
Todd Murphey
金额：
$ 89.6万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837515&HistoricalAwards=false
关键词：
CPS Medium Information based Control

项目摘要

Most cyber-physical systems have operated in comparatively benign environments, often engineered to meet the needs of the system. For instance, in many settings robots operate in closed-off environments in manufacturing. However, as these systems are deployed in increasingly isolated environments (such as search and rescue efforts, automation in surgical devices, collaborative manufacturing) these robots will need to operate while reasoning about substantial uncertainty. Moreover, actions taken by the system impact that uncertainty. The proposed work will develop algorithms that enable a cyber-physical system to reason about what actions it must take to manage its uncertainty. A simple example of this is understanding that one must look behind and under things when searching for an object. The goal of this project is to automate the process of managing that uncertainty, however it arises. For instance, interacting with humans (such as physically interacting with a person while assisting her motion, or tracking a person during a search effort) involves uncertainty about what the person is going to do. Enabling a robotic system to actively test a person's intent, and then act according to her subsequent behavior, is key to the robot's ability to be an effective partner. This essential point, that action on the part of a cyber-physical system can be used to explicitly manage its understanding of the world, is the core purpose of the proposed work.The proposed work will leverage recent results in information-based real-time nonlinear control. Specifically, ergodic control (controlling the ergodicity of a trajectory relative to some reference distribution) enables one to specify an objective using a density function to describe the desired spatial characteristics for a trajectory. In the context of Hidden Markov Models (HMMs) and Partially Observable Markov Decision Processes (POMDPs), this allows a system to actively probe multiple states, simultaneously considering process uncertainty, measurement uncertainty, and uncertainty associated with the Markov process itself, even when the HMM is changing over time because of new states or processes being introduced into the ambient environment. The apparent reason for the effectiveness of ergodic control in the context of reactive planning under uncertainty is that it always computes plans in the continuum, avoiding the combinatoric complexity associated with POMDPs. Moreover, the needs of the dynamic system and the cyber system are inter-related during physical motion, so the work will additionally develop methods that maintain stability with respect to both state and information. This inter-dependence creates a trade-off between the physical ability to act and the computational load on the cyber system, enabling a cyber-physical system to reduce the load on its cyber system through physical action. The research will develop algorithms capable of real-time information-based control in response to constantly changing data. One of the motivating examples will be aerial vehicles tracking unknown numbers of targets on the ground in a highly occluded environment such as a forest or an urban setting. Additionally, a robotic arm, already used for assistive device research, will be used to implement and assess the proposed methods.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.

大多数网络物理系统都在相对良好的环境中运行，通常是为了满足系统的需求而设计的。例如，在许多环境中，机器人在制造过程中的封闭环境中运行。然而，随着这些系统部署在日益孤立的环境中（例如搜索和救援工作、手术设备自动化、协作制造），这些机器人将需要在推理大量不确定性的同时进行操作。此外，系统采取的行动会影响这种不确定性。拟议的工作将开发算法，使网络物理系统能够推理出必须采取哪些行动来管理其不确定性。一个简单的例子是，理解在寻找物体时必须查看物体的后面和下面。该项目的目标是自动化管理不确定性的过程，无论它如何出现。例如，与人类互动（例如在协助某人运动时与某人进行身体互动，或在搜索过程中跟踪某人）涉及到该人将要做什么的不确定性。让机器人系统能够主动测试人的意图，然后根据她随后的行为采取行动，是机器人成为有效合作伙伴的关键。这一要点是网络物理系统的行动可用于明确管理其对世界的理解，这是拟议工作的核心目的。拟议工作将利用基于信息的实时的最新成果非线性控制。具体来说，遍历控制（控制轨迹相对于某些参考分布的遍历性）使人们能够使用密度函数来指定目标，以描述轨迹的所需空间特征。在隐马尔可夫模型 (HMM) 和部分可观察马尔可夫决策过程 (POMDP) 的背景下，这允许系统主动探测多个状态，同时考虑过程不确定性、测量不确定性以及与马尔可夫过程本身相关的不确定性，即使当由于周围环境中引入了新的状态或过程，HMM 会随着时间的推移而发生变化。在不确定性下的反应性规划中，遍历控制有效的明显原因是它总是在连续体中计算计划，避免了与 POMDP 相关的组合复杂性。此外，动态系统和信息系统的需求在物理运动过程中是相互关联的，因此该工作将另外开发保持状态和信息稳定性的方法。这种相互依赖性在物理行动能力和网络系统的计算负载之间建立了权衡，使网络物理系统能够通过物理行动减少其网络系统的负载。该研究将开发能够基于信息进行实时控制的算法，以响应不断变化的数据。激动人心的例子之一是飞行器在森林或城市等高度遮挡的环境中跟踪地面上未知数量的目标。此外，已用于辅助设备研究的机械臂将用于实施和评估所提出的方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Measuring Human-Robot Team Benefits Under Time Pressure in a Virtual Reality Testbed

在虚拟现实测试台中测量时间压力下的人机团队效益

DOI：
10.1109/iros55552.2023.10341794
发表时间：
2023-10-01
期刊：
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
Katarina Popovic;Millicent Schlafly;A. Prabhakar;Christopher Kim;Todd D. Murphey
通讯作者：
Todd D. Murphey

Operation and Imitation under Safety-Aware Shared Control

安全共享控制下的操作与模仿