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.

该项目追求一种具有突破性潜力的完全自动驾驶车辆运动控制方法，该方法允许仅使用污染物浓度测量来寻找、查找和跟踪污染物场源，而不需要 GPS 或 INS信息。这种方法建立在 PI 在通过极值搜索控制进行实时优化领域的最新进展的基础上。该工作重点完成以下任务：(A) 将提案中提出的初步发现从移动机器人的 2D 运动扩展到水下和空中航行器的 3D 运动； (B) 使用平均和奇异扰动技术对方案进行收敛研究； (C) 对湍流（而不仅仅是扩散）流体场中的结果进行数值测试； (D) 测试方案的“污染物清洁”能力，其中通过释放中和污染物的试剂，沿跟踪器行进的路径暂时降低污染物浓度； (E) 将结果从单一污染物源和单一追踪器扩展到多个污染物源和多个追踪器； (F) 用移动机器人在模拟污染物场的光场中进行实验； (G) 在环境室中使用浓度传感器（烟雾）进行实验。