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.

在这个项目中，追求一种具有开创性潜力的方法，用于完全自主车辆的运动控制，该方法允许仅使用污染物浓度的测量而不需要GPS或INS信息来寻找、发现和跟踪污染物场的源。这种方法建立在PI通过极值搜索控制在实时优化领域的最新进展的基础上。本文的工作主要集中在以下几个方面：（A）将方案中提出的最初发现从移动的机器人的二维运动扩展到水下和空中飞行器的三维运动;（B）使用平均和奇异摄动技术对方案进行收敛性研究;（C）在湍流中对结果进行数值检验（D）测试方案的“污染物清除”能力，其中通过释放中和污染物的试剂，污染物浓度沿跟踪器行进的路径沿着暂时降低;（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用移动的机器人进行实验;（G）在环境室中用浓度传感器（用于烟雾）进行实验。