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

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• 批准号: 1724016
• 负责人: Mongying Hsieh
• 金额: $ 34.69万
• 依托单位: Drexel 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 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724016&HistoricalAwards=false
• 关键词: S& FND; COLLAB; Planning; Control

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被设想用于商业、科学探索、搜索和救援以及许多其他任务。然而，机器人在这样的环境中面临着重大挑战。车辆必须在具有大惯性效应和干扰的环境中行动和影响变化，只有近场感知。再加上我们对海洋动力学的有限理解以及缺乏可访问和高质量的洋流数据，这些障碍使得在这些不同的应用中使用机器人技术变得极其困难。该项目实现了一种集成的、异构的机器人方法，用于环境缓解和搜救行动的大规模海洋监测。它可以对海洋环境中各种物理、化学和/或生物过程进行数据驱动的跟踪和映射，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，并改善世界海洋的健康状况。该项目的成果通过pi的课程，指导学生进行研究，并扩大现有的K-12外展关系，整合到教育中。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控、感知和操作的新方法的基础知识。该项目为异构监测机器人团队开发新方法，通过当前与环境的交互来改进其环境模型；同时收集数据，将其处理和吸收到现有模型中，并在该模型上进行规划；接受高级指令，并将环境监测等目标导向指令转换为合适的计划，以便通过底层系统架构进行感知、推理、沟通和行动；并监控它们的行为，自主地优化和重新配置。提出的异质机器人团队包括在海气界面提供样本的水面机器人和创建流动模型并在团队中充当中介的空中机器人。该方法的层次结构在创建分布式感知框架时利用了拉格朗日相干结构定义的自然边界。