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

基本信息

批准号：
2311967
负责人：
Nora Ayanian
金额：
$ 35.31万
依托单位：
Brown University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311967&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.

海洋和沿海环境中的机器人有望用于商业、科学探索、搜索和救援以及许多其他任务。然而，在这种环境中的机器人面临着巨大的挑战。车辆必须在具有较大惯性效应和干扰的环境中行动并影响变化，并且仅具有近场感知。再加上我们对海洋动力学的了解有限，并且缺乏可获取的高质量海洋流量数据，这些障碍使得机器人技术在这些不同的应用中的使用变得极其困难。该项目实现了一种集成的、异构的机器人方法，用于大规模海洋监测，用于环境缓解和搜救行动。它能够以数据驱动的方式跟踪和绘制海洋环境中感兴趣的各种物理、化学和/或生物过程，例如跟踪污染物扩散或失踪的飞机。该项目显着提高了海洋搜索和监测技术的先进水平，帮助我们了解和利用洋流，并改善世界海洋的健康。该项目的成果通过 PI 课程融入教育，指导学生进行研究，并扩大现有的 K-12 外展关系。该项目创造了有关机器人在动态和不确定的环境中更好地监控、感知和操作的新方法的基础知识。该项目为异构监控机器人团队开发了新方法，通过当前与环境的交互来改进其环境模型；同时收集数据、处理数据并将其吸收到现有模型中，并针对该模型进行规划；接受高层指令，并将环境监测等目标导向指令转化为合适的计划，通过底层系统架构进行感知、推理、通信和行动；并自主监控他们的行为、优化和重新配置。提出的异构机器人团队包括在海空界面提供样本的地面车辆和创建流动模型并充当团队内中介的空中机器人。该方法的层次结构在创建分布式传感框架时利用拉格朗日相干结构定义的自然边界。