ERI: Adaptive and Resilient Communication-Aware Multi-Robot Coordination

ERI：自适应和弹性通信感知多机器人协调

• 批准号: 2301749
• 负责人: Wenhao Luo
• 金额: $ 20万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301749&HistoricalAwards=false
• 关键词: ERI; Adaptive; Resilient; Communication; Aware

This Engineering Research Initiation (ERI) grant will support fundamental research that aims to improve reliable coordination for a team of autonomous mobile robots operating in realistic and possibly adversarial environments. Robots that can collaborate have shown great potential in many applications from search and rescue missions to precision agriculture. Due to limited sensing, communication, and processing capabilities, autonomous robots in a collaborating group often need to make collective decisions through communications in proximity. For example, exchanging sensing data or control commands with neighboring robots through range-constrained wireless communications. The integral role of wireless communications requires robots to coordinate and react to the changing environment for reliable information sharing in addition to their original tasks. However, existing research paradigm often assumes that the communication environment is well modeled and can be pre-programmed into robotic systems for effective coordination without disruptions. This significantly limits the capabilities of robot teams and makes them vulnerable in real-world environments that can often be unpredictable. To address the challenges, this project seeks to create a set of methods and tools that may augment many multi-robot coordination tasks, by enabling robots to learn the environment on the fly, adapt their motion to environmental changes, and maintain communications when unexpected robot failures happen. The project will also support education and outreach activities such as curriculum development, broadening participation of students from underrepresented groups, and local community engagement in the Charlotte metropolitan area.The objective of this project is to create novel methods and algorithms that enable the co-design of learning, communication, and motion control for mobile robot teams. This may allow for robust operation of robots with provable assurances on communication capabilities and multi-robot network resilience adaptive to uncertain environments, positively supporting the primary task execution. In pursuit of this goal, the project will make two main contributions: (i) Developing data-driven methods for robots to collaboratively learn the spatially varying realistic communication performance online, and (ii) Developing new approaches that specify the impact of realistic communication constraints and network resilience on the task-related robots’ motion, to enable joint optimization for more efficient multi-robot coordination with performance guarantees. The work will be evaluated in simulations and experiments on physical robotic platforms. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

这项工程研究启动(ERI)拨款将支持基础研究，旨在改善在现实环境中运行的自主移动机器人团队的可靠协调。可以协作的机器人在从搜救任务到精准农业的许多应用中都显示出了巨大的潜力。由于感知、通信和处理能力有限，协作群体中的自主机器人经常需要通过附近的通信来做出集体决策。例如，通过距离受限的无线通信与邻近的机器人交换传感数据或控制命令。无线通信的不可或缺的作用要求机器人在最初的任务之外，还必须协调和应对不断变化的环境，以实现可靠的信息共享。然而，现有的研究范式往往假设通信环境经过良好的建模，并可以预先编程到机器人系统中，以便在不中断的情况下进行有效的协调。这大大限制了机器人团队的能力，使他们在经常不可预测的真实世界环境中变得脆弱不堪。为了应对这些挑战，该项目寻求创建一套方法和工具，通过使机器人能够动态学习环境，使其运动适应环境变化，并在发生意外的机器人故障时保持通信，从而增强许多多机器人协调任务。该项目还将支持教育和外展活动，如课程开发，扩大来自代表性不足群体的学生的参与，以及夏洛特大都市区的当地社区参与。该项目的目标是创建新的方法和算法，使移动机器人团队能够共同设计学习、交流和运动控制。这可能允许机器人在通信能力上具有可证明的保证的健壮操作，以及适应不确定环境的多机器人网络弹性，积极地支持主要任务的执行。为了实现这一目标，该项目将做出两个主要贡献：(I)为机器人开发数据驱动的方法，以便在线协作学习空间变化的现实通信性能；(Ii)开发新的方法，指定现实通信约束和网络弹性对与任务相关的机器人运动的影响，以实现关节优化，在保证性能的情况下更有效地进行多机器人协调。这项工作将在物理机器人平台上的模拟和实验中进行评估。该项目由跨部门机器人基础研究计划支持，该计划由工程学指导委员会(ENG)和计算机与信息科学与工程指导委员会(CEISE)共同管理和资助。该奖项反映了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。