Energy-Efficient, Multi-Scale, Biologically-Inspired Mobile Sensor Networks with Real-Time Observation Adaptability

具有实时观测适应性的节能、多尺度、受生物启发的移动传感器网络

• 批准号: 0501407
• 负责人: Joseph Bentsman
• 金额: $ 24万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2010-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0501407&HistoricalAwards=false
• 关键词: Energy; Efficient; Multi; Scale; Biologically

ECS-0501407Energy-Efficient, Multi-Scale, Biologically-Inspired Mobile Sensor Networks with Real-Time Observation AdaptabilityThis integrative systems proposal focuses on the cooperative propulsion of hydrodynamically-coupled biomimetic underwater vehicles. The superiority of the proposed platform hinges on the integration of flow sensing into the closed-loop control of vehicle-wake interactions. A fish-like robot will be constructed that responds in real time to ambient flows estimated using micro-machined hair cells distributed along the vehicle's sides. Computational multi-scale algorithms will be developed to enable the rapid extraction of relevant flow information from sensor data. Analytical and computational models will be derived to clarify the principles by which fish exploit ambient flows for efficient swimming, focusing on schooling behavior. These principles will be used to design feedback control laws for efficient robotic locomotion through unsteady flows. Fast approximate dynamic programming algorithms will then be developed for the redirection and reshaping of vehicle schools to maximize the collection of information from time-varying sources. The intellectual merit of the research proposed is in creating analytical and computational tools and technology that will enable schools of vehicles to exploit principles of unsteady flow to achieve energy efficient locomotion and maneuvering for adaptive sensing.The broad impact of the research is in providing the basis for autonomous mobile sensor arrays to be deployed over unprecedented distances for underwater applications ranging from environmental sampling to the collection of military intelligence. This project will also enhance the interdisciplinary engineering curriculum at UIUC and directly train one PhD student and numerous undergraduates.

ECS-0501407节能、多尺度、受生物启发、具有实时观测适应性的移动传感器网络这一集成系统方案侧重于流体动力耦合仿生水下机器人的协作推进。所提出的平台的优势在于将流量传感集成到车辆-尾流相互作用的闭环控制中。将建造一个像鱼一样的机器人，通过分布在车辆侧面的微机械毛细胞估计周围的流动，实时做出反应。将开发计算性多尺度算法，以实现从传感器数据中快速提取相关流量信息。分析和计算模型将被用来阐明鱼类利用环境流进行高效游泳的原理，重点是鱼群行为。这些原理将被用来设计反馈控制律，以实现机器人在非定常流动中的高效运动。然后，将开发快速近似动态规划算法，用于车辆学校的重新定向和重塑，以最大限度地收集来自时变来源的信息。这项研究的智力价值在于创造了分析和计算工具和技术，使车辆学校能够利用非恒定流原理来实现节能的移动和机动，以实现自适应感知。这项研究的广泛影响在于为自主移动传感器阵列在前所未有的距离上部署提供基础，用于从环境采样到军事情报收集等水下应用。该项目还将加强UIUC的跨学科工程课程，并直接培训一名博士生和许多本科生。