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

• 批准号: 1715714
• 负责人: Xiaobo Tan
• 金额: $ 26.5万
• 依托单位: Michigan State 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=1715714&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Information

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.

该项目开发了用于移动传感平台自动导航（例如无人接地，空中和水下车辆）的理论和算法，从而最大程度地提高了收集到的信息，同时容纳了运动能力和能源消耗的限制。这项工作促进了在环境监测，搜索和救援，监视和安全性以及社会重要性的应用中使用自主机器人。使用独特的滑翔机器人鱼类对算法进行评估。 该项目为研究生和本科生提供培训，包括来自代表性不足的小组的培训。通过在芝加哥的科学与工业博物馆展示并提供开源机器人鱼教育套件，该项目促进了K-12学生和科学与工程学的普通公众的兴趣。该项目进一步促进了用于机器人传感到市场的软件和硬件的传输。该项目的目的是弥合信息驱动的移动感测的理论和实践之间的差距，并为不确定的不确定的自主探索而开发一个原则性的理论和算法框架，以不确定的是不确定的，具体的水下环境，环境。该方法利用了沿阵行探索的概念，其中使用非线性最佳控制方法解决了基础优化问题。该研究包括：1）建立一个严格的理论框架，以保证解决方案和稳定性良好的解决方案，并开发用于实时控制的合成方法； 2）通过辅助测量示踪剂探索流动条件的主动探测，以减轻环境不确定性； 3）调查协作探索计划，以达到绩效，复杂性和鲁棒性之间的平衡； 4）进行现场实验，使用一组滑行机器人鱼来验证框架，以监测有害的藻华和定位化学溢出源。

项目成果

Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems

• DOI: 10.1109/tro.2021.3076581
• 发表时间: 2021-12-01
• 期刊: IEEE TRANSACTIONS ON ROBOTICS
• 影响因子: 7.8
• 作者: Mamakoukas, Giorgos;Castano, Maria L.;Murphey, Todd D.
• 通讯作者: Murphey, Todd D.

Time-difference-of-arrival (TDOA)-based distributed target localization by a robotic network

机器人网络基于到达时间差 (TDOA) 的分布式目标定位

• DOI: 10.1109/tcns.2020.2979864
• 发表时间: 2020
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Ennasr, Osama N.;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

Backstepping Control of Gliding Robotic Fish for Trajectory Tracking in 3D Space

• DOI: 10.23919/acc45564.2020.9147628
• 发表时间: 2020-07
• 期刊: 2020 American Control Conference (ACC)
• 作者: Demetris Coleman;Xiaobo Tan

Backstepping Control-based Trajectory Tracking for Tail-actuated Robotic Fish

基于反步控制的尾驱动机器鱼轨迹跟踪

• DOI: 10.1109/aim.2019.8868586
• 发表时间: 2019
• 期刊: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM
• 作者: Castano, Maria L.;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo

Adaptive Parameter Estimation of a Steerable Drifter

可操纵漂流器的自适应参数估计

• DOI: 10.1016/j.ifacol.2021.11.161
• 发表时间: 2021
• 期刊: IFAC-PapersOnLine
• 作者: Gaskell, Eric;Tan, Xiaobo
• 通讯作者: Tan, Xiaobo