Contaminant Tracking in GPS-Denied Environments

GPS 无法识别的环境中的污染物追踪

• 批准号: 0653834
• 负责人: Miroslav Krstic
• 金额: $ 24万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0653834&HistoricalAwards=false
• 关键词: Contaminant; Tracking; GPS; Denied; Environments

An approach with ground breaking potential for motion control of completely autonomous vehicles is pursued in this project, which allows to seek, find, and follow a source of a contaminant field using only the measurement of the concentration of the contaminant and not requiring GPS or INS information. This approach builds on recent advances by the PI in the area of real-time optimization via extremum seeking control. The work focuses on the following tasks: (A) extending the initial discovery presented in the proposal from 2D locomotion for mobile robots to 3D locomotion for underwater and aerial vehicles; (B) performing a convergence study for the schemes using averaging and singular perturbation techniques; (C) numerically testing the results in turbulent (rather than merely diffusive) fluid fields; (D) testing of "contaminant cleaning" abilities of the schemes where the contaminant concentration is reduced temporarily along the path traveled by the tracker by releasing an agent that neutralizes the contaminant; (E) extending the results from a single contaminant source and single tracker to multiple contaminant sources and multiple trackers; (F) performing experiments with a mobile robot in an optical field emulating a contaminant field; (G) performing experiments with concentration sensors (for smoke) in an environmental chamber.

在本项目中采用了一种针对完全自动驾驶汽车进行运动控制的突出潜力的方法，该方法仅使用污染物的浓度的测量，而不需要GPS或INS，从而允许寻找，查找和遵循污染物场的来源信息。这种方法是基于PI在实时优化领域的最新进展，通过寻求极端的控制。该工作重点介绍以下任务：（a）将提案中提出的初始发现从移动机器人的2D运动扩展到水下和航空车的3D机动； （b）使用平均和奇异的扰动技术对方案进行收敛研究； （c）在湍流（而不是仅仅是扩散）流体场中测试结果； （d）测试方案的“污染物清洁”能力，其中通过释放一种中和污染物的试剂，沿着追踪器传播的路径暂时降低了污染物的浓度； （e）将结果从单个污染物源和单个跟踪器扩展到多个污染物源和多个跟踪器； （f）在模拟污染物场的光场中使用移动机器人进行实验； （g）在环境室内用浓度传感器（用于烟雾）进行实验。