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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。多机器人系统由自主机器人组成，它们在共享环境中相互作用，以实现共同目标。它们被广泛应用于交通、灾害管理以及仓储和制造等实际应用领域。该项目开发了一个高效、健壮、安全的多机器人系统，称为fi。EdgeRobot建立了一个基于边缘计算的架构和算法框架，以促进动态环境中多机器人的协作和协调。这项工作为协调多机器人系统提供了新的模型、体系结构和理论。此外，该项目还建立了可持续的研究能力，通过美国六所为少数族裔服务的机构建立伙伴关系，这些机构是：休斯顿-克利尔湖大学(南部)、密歇根大学弗林特大学(北部)、纽约州立大学-纽约市理工学院(东北部)、摩根州立大学(东部)、旧金山州立大学(西部)和加州州立大学多明格斯山庄(西部)。跨机构合作不仅提高了所有六所参与机构的研究能力，还为代表人数较少的少数族裔学生提供了综合研究和教育经验。最终，该项目建立和示范了一种有效的合作模式，用于培训和教育来自不同地理位置的少数民族服务机构的代表不足的学生。该项目包括以下三个研究主旨。首先，新的边缘计算基础设施为协作机器人提供最优的位置感知计算服务，以实现它们的共同目标。此外，基于强化学习的算法解决了多机器人调度和路径问题，将其建模为领奖旅行商问题的变体。其次，在需要协作动作的任务中，如协作目标跟踪，多智能体强化学习使机器人团队能够在动态和人口稠密的环境中稳健而安全地操作、学习和适应。第三，融合了现代密码学和安全原语，保证了多机器人系统中边缘节点之间的协作。因此，EdgeRobot和它的人类团队成员之间的接口建立了一个共享的自主模型。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.