Collaborative Research: CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments

协作研究：CISE-MSI：DP：CNS：动态环境中鲁棒多机器人系统的基于边缘的方法

• 批准号: 2240516
• 负责人: Lili Ma
• 金额: $ 8万
• 依托单位: CUNY New York City College of Technology
• 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240516&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions.This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。多机器人系统由在共享环境中进行交互的自主机器人组成，以实现共同的目标。它们被广泛用于现实的应用程序领域，例如运输，灾难管理以及仓储和制造。该项目开发了一个效率，健壮和安全的多机器人系统，称为Edgerobot。 Edgerobot建立了基于边缘计算的体系结构和算法框架，以支持动态环境中的多机器人协作和协调。这项工作为协调的多机器人系统提供了新的模型，体系结构和理论。此外，该项目还建立了研究能力，可持续用于培训代表性不足的学生通过在美国的六个地理上多元化的少数族裔服务机构的合作伙伴关系：休斯顿大学 - 北弗林特大学（北），北纽约市纽约州弗林特大学（北），北纽约纽约市，东北大学，摩根尼岛大学，摩根尼群岛，纽约州立大学（西部），西弗朗西斯科大学，西弗朗西斯科大学，西部。 （西）。跨机构的合作不仅具有所有六个参与机构的研究能力，而且还为代表性不足的少数群体学生提供综合的研究和教育经验。最终，该项目建立并展示了一种有效的合作模型，用于培训和教育来自地理上不同少数族裔服务机构的代表性不足的学生。该项目由以下三项研究组成。首先，新颖的边缘计算基础架构为协作机器人提供了最佳和位置感知的计算服务，以实现其共同目标。此外，基于增强学习的算法解决了多机器人调度和路由问题，以奖品收集的旅行推销员问题为模型。其次，在需要协作行动的任务中，例如可教练的目标跟踪，多机构增强学习，使机器人团队能够在动态和人居住的环境中运行，学习和适应。第三，集成现代的加密和安全原始图可以确保多机器人系统中边缘节点之间的协作。因此，Edgerobot及其人类团队成员之间的界面建立了共同的自治模型。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，通过评估诚实地表示支持。