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RI: Small: Collaborative Research: Information-driven Autonomous Exploration in Uncertain Underwater Environments

RI：小型：协作研究：不确定水下环境中信息驱动的自主探索

基本信息

批准号：
1717951
负责人：
Todd Murphey
金额：
$ 23.47万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717951&HistoricalAwards=false
关键词：
RI Small Collaborative Research Information

项目摘要

This project develops the theory and algorithms for autonomous navigation of mobile sensing platforms, such as unmanned ground, aerial, and underwater vehicles, so that the collected information is maximized while constraints on movement capabilities and energy expenditure are accommodated. This work facilitates the use of autonomous robots in environmental monitoring, search and rescue, surveillance and security, among other applications of societal importance. The algorithms are evaluated in field trials using unique gliding robotic fish. The project provides training for both graduate and undergraduate students, including those from underrepresented groups. Through showcasing at the Museum of Science and Industry in Chicago and offering of an open-source robotic fish education kit, the project promotes the interest of K-12 students and the general public in science and engineering. The project further facilitates transfer of software and hardware for robotic sensing to the market.The goal of this project is to bridge the gap between the theory and practice in information-driven mobile sensing and to develop a principled theoretic and algorithmic framework for autonomous exploration in uncertain, specifically underwater, environments. The approach exploits the concept of ergodic exploration, where the underlying optimization problem is solved using methods from nonlinear optimal control. The research consists of: 1) Establishing a rigorous theoretical framework for ergodic exploration for guaranteeing solution well-posedness and stability, and developing synthesis methods for real-time control; 2) exploring active probing of flow conditions via auxiliary measurement of tracer agents to mitigate environmental uncertainty; 3) investigating collaborative exploration schemes that strike balance among performance, complexity, and robustness; and 4) conducting field experiments to validate the framework using a group of gliding robotic fish to monitor harmful algal blooms and localize sources of chemical spills.

该项目开发了无人地面、空中和水下机器人等移动传感平台的自主导航理论和算法，以便在满足移动能力和能量消耗的限制的同时，最大化收集到的信息。这项工作促进了自主机器人在环境监测、搜索和救援、监视和安全以及其他具有社会重要性的应用中的使用。这些算法在现场试验中使用了独特的滑行机器鱼进行了评估。该项目为研究生和本科生提供培训，包括那些来自代表性不足群体的学生。通过在芝加哥科学与工业博物馆展出并提供开源机器鱼教育工具包，该项目促进了K-12学生和普通公众对科学和工程的兴趣。该项目进一步促进了机器人传感的软件和硬件向市场的转移。该项目的目标是弥合信息驱动的移动传感理论和实践之间的差距，并为不确定的环境，特别是水下环境中的自主探索开发一个原则性的理论和算法框架。该方法利用遍历探索的概念，其中潜在的优化问题是使用来自非线性最优控制的方法来解决的。研究包括：1)建立严格的遍历探测理论框架，以确保解的适定性和稳定性，并开发实时控制的综合方法；2)通过示踪剂的辅助测量来探索流动条件的主动探测，以减轻环境的不确定性；3)研究在性能、复杂性和稳健性之间取得平衡的协作探测方案；以及4)进行现场实验，以使用一组滑翔的机器鱼来监测有害的藻华和定位化学泄漏的源。