RII Track-4: NSF: Enabling Synergistic Multi-Robot Cooperation for Mobile Manipulation Beyond Individual Robotic Capabilities

RII Track-4：NSF：实现协同多机器人合作，实现超越单个机器人能力的移动操作

• 批准号: 2327313
• 负责人: Hongsheng He
• 金额: $ 30万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327313&HistoricalAwards=false
• 关键词: RII; Track; NSF; Enabling; Synergistic

Complex robotic missions may require access to and exploration of more challenging sites than typical workspaces, including steep and broken terrain, subsurface voids, and other extreme locations. These missions often surpass the mobility and perception capabilities of individual robots, such as force and payload capacities. However, these missions could potentially be accomplished by a multi-robot team that coordinates actions and applies effective forceful interaction. To specifically address the problem of multi-robot cooperation in challenging sites, the project will develop an efficient coordination mechanism for cooperative mobile manipulation in unknown, dynamic, or demanding environments. This project has potential to transform and expand the investigator's research scope from dexterous multi-fingered manipulation to mobile manipulation based on a team of collaborative mobile robots. Additionally, the project will advance the research infrastructure and capacity of the investigator's home institution, and the transformative approach could be scalable to general problems of interest to NASA and local companies.This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows project will provide a fellowship to an Assistant professor and training for a graduate student at the Wichita State University. This work will be conducted in collaboration with researchers at the Jet Propulsion Laboratory. The project will develop a Synergistic Robotic Coordination framework that enables formal representation, learning, and planning in cooperation strategies to accomplish physically challenging robotic missions. The framework is characterized by tightly coupling and symbiotic coordination strategies of multi-robots, with the aim of generating synergistic effects through creative coordination strategies, including anchoring, tugging, tilting, wedging, sliding, spinning, and pivoting. To establish mutual awareness, a multimodal multi-rate fusion algorithm for heterogeneous sensors will be designed for positioning, and temporal synchronization algorithms will be developed to coordinate multi-robot actions. A reinforcement logical learning architecture that utilizes a "knowledge network" to expedite learning and incorporates first-order logic constraints will be designed to implement SRC strategies. The project has the potential to transform the understanding of multi-robot coordination by introducing a perspective of formal strategy representation and knowledge-driven learning.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.

复杂的机器人任务可能需要与典型的工作区（包括陡峭和破碎的地形，地下空隙和其他极端位置）相比，需要访问和探索更具挑战性的站点。这些任务通常超过单个机器人（例如武力和有效载荷能力）的流动性和感知能力。但是，这些任务有可能由一个多机器人团队来协调行动并采用有效的有效互动来完成。为了具体解决在具有挑战性的站点中多机器人合作的问题，该项目将开发出一种有效的协调机制，用于在未知，动态或苛刻的环境中进行合作移动操作。该项目有潜力将研究者的研究范围从灵巧的多指操作转换为基于协作移动机器人团队的移动操作。此外，该项目将推动研究人员家庭机构的研究基础设施和能力，并且可以扩展到NASA和本地公司感兴趣的一般问题。这项研究基础设施改进Track-4 EPSCOR Research Fellows项目将为Wichita State State Lessive的助理教授和培训提供研究生研究。这项工作将与JET推进实验室的研究人员合作进行。该项目将开发一个协同的机器人协调框架，该框架可以实现合作策略的正式代表，学习和计划，以完成身体上挑战的机器人任务。该框架的特征是多机器人的紧密耦合和共生协调策略，目的是通过创造性的协调策略来产生协同效果，包括锚定，拖曳，倾斜，倾斜，楔子，滑动，滑动，旋转和旋转和旋转。为了建立相互意识，将设计用于定位的多模式多速率融合算法，并将开发时间同步算法来协调多机器人动作。使用“知识网络”来加快学习并结合一阶逻辑约束的强化逻辑学习体系结构将被设计为实施SRC策略。该项目有可能通过介绍正式战略代表和知识驱动的学习的观点来改变对多机器人协调的理解。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子的智力优点和更广泛的影响来通过评估来支持的。