权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

S&AS: FND: COLLAB: Planning and Control of Heterogeneous Robot Teams for Ocean Monitoring

基本信息

批准号：
1812319
负责人：
Mongying Hsieh
金额：
$ 34.69万
依托单位：
University of Pennsylvania
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 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812319&HistoricalAwards=false
关键词：
S&amp FND COLLAB Planning Control

项目摘要

Robots in marine and littoral environments are envisioned for commerce, scientific exploration, search and rescue, and many other tasks. However, robots in such environments face significant challenges. Vehicles must act and effect change in environments with large inertial effects and disturbances, with only near-field perception. Coupled with our limited understanding of ocean dynamics and the lack of accessible and high-quality ocean flow data, these obstacles make the use of robotics technology in these varied applications extremely difficult. This project realizes an integrated, heterogeneous robotic approach towards large-scale ocean monitoring for environmental mitigation and search and rescue operations. It enables data-driven tracking and mapping of various physical, chemical, and/or biological processes of interest in marine environments, such as tracking contaminant dispersion or missing aircraft. This project significantly improves the state of the art in ocean search and monitoring technology, helping us understand and harness ocean currents, and improve the health of the world's oceans. Results from the project are integrated into education, through the PIs' courses, mentoring students on research, and expanding an existing K-12 outreach relationship. The project creates fundamental knowledge about new ways that robots can better monitor, sense, and operate in dynamic and uncertain environments. The project develops new methods for heterogeneous teams of monitoring robots to improve their environment model through current interactions with the environment; concurrently collect data, process and assimilate it into the existing model, and plan on that model; accept high-level instruction and translate goal-oriented directives such as environmental monitoring into a suitable plan for sensing, reasoning, communicating, and acting through the underlying system architecture; and monitor their actions, optimize, and reconfigure autonomously. The heterogeneous team of robots proposed includes surface vehicles providing samples at the air-sea interface and aerial robots creating flow models and acting as intermediaries within the team. The hierarchical structure of the approach takes advantage of the natural boundaries defined by Lagrangian coherent structures in the creation of a distributed sensing framework.

海洋和沿岸的环境中的机器人预计将用于商业、科学探索、搜索和救援以及许多其他任务。然而，在这样的环境中，机器人面临着巨大的挑战。车辆必须在具有大惯性效应和干扰的环境中采取行动并实现变化，只有近场感知。再加上我们对海洋动力学的了解有限，缺乏可获得的高质量洋流数据，这些障碍使得机器人技术在这些不同的应用中的使用极为困难。该项目实现了一个综合的、异构的机器人方法，用于环境缓解和搜索救援行动的大规模海洋监测。它可以对海洋环境中感兴趣的各种物理，化学和/或生物过程进行数据驱动的跟踪和绘图，例如跟踪污染物扩散或失踪飞机。该项目大大提高了海洋搜索和监测技术的最新水平，帮助我们了解和利用洋流，改善世界海洋的健康状况。该项目的成果通过PI的课程融入教育，指导学生进行研究，并扩大现有的K-12外联关系。该项目创造了关于机器人在动态和不确定环境中更好地监测，感知和操作的新方法的基础知识。该项目为监测机器人的异构团队开发新方法，通过当前与环境的交互来改进其环境模型;同时收集数据，处理并将其同化到现有模型中，并对该模型进行规划;接受高层次的指导，并将环境监测等目标导向的指令转化为适当的感知，推理，沟通，并通过底层系统架构进行操作;并监控它们的操作、自动优化和重新配置。提出的机器人的异质团队包括在海气界面提供样本的地面车辆和空中机器人创建流模型，并作为团队内的中介。该方法的层次结构利用拉格朗日相干结构定义的自然边界，在创建一个分布式传感框架。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Flow-Based Rendezvous and Docking for Marine Modular Robots in Gyre-Like Environments

类似环流环境中的海洋模块化机器人基于流的交会对接

DOI：
发表时间：
2023
期刊：
IEEE International Conference on Robotics and Automation (ICRA2023
影响因子：
0
作者：
Knizhnik, G.;Li, P.;Yim, M.;Hsieh, M. A.
通讯作者：
Hsieh, M. A.

Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness

学习和利用类似流程的环境中的功能来提高态势感知

DOI：
10.1109/lra.2022.3141762
发表时间：
2022
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Salam, Tahiya;Edwards, Victoria;Hsieh, M. Ani
通讯作者：
Hsieh, M. Ani

Adaptive Sampling and Reduced-Order Modeling of Dynamic Processes by Robot Teams

机器人团队动态过程的自适应采样和降阶建模

DOI：
10.1109/lra.2019.2891475
发表时间：
2019
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Salam, Tahiya;Hsieh, M. Ani
通讯作者：
Hsieh, M. Ani

Asynchronous Adaptive Sampling and Reduced-Order Modeling of Dynamic Processes by Robot Teams via Intermittently Connected Networks

机器人团队通过间歇连接网络对动态过程进行异步自适应采样和降阶建模

DOI：
10.1109/iros45743.2020.9341636
发表时间：
2020
期刊：
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
影响因子：
0
作者：
Rovina, Hannes;Salam, Tahiya;Kantaros, Yiannis;Ani Hsieh, M.
通讯作者：
Ani Hsieh, M.