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

复杂的机器人任务可能需要进入和探索比典型的地形更具挑战性的地点，包括陡峭和破碎的地形，地下空洞和其他极端位置。这些任务往往超过了单个机器人的机动性和感知能力，例如力量和有效载荷能力。然而，这些任务可能由一个多机器人团队来完成，该团队协调行动并应用有效的强制交互。为了具体解决多机器人在具有挑战性的网站合作的问题，该项目将开发一个高效的协调机制，在未知的，动态的，或苛刻的环境中的合作移动的操作。本计画将可将研究者的研究范围从灵巧多指操作，转换及扩展至基于一组合作式移动的机器人的移动的操作。此外，该项目将提高研究人员所在机构的研究基础设施和能力，并且这种变革性方法可以扩展到NASA和当地公司感兴趣的一般问题。这个研究基础设施改进轨道-4 EPSCoR研究员项目将为一名助理教授提供奖学金，并为威奇托州立大学的一名研究生提供培训。这项工作将与喷气推进实验室的研究人员合作进行。该项目将开发一个协同机器人协调框架，使正式的表示，学习和规划合作策略，以完成具有物理挑战性的机器人任务。该框架的特点是紧耦合和共生的多机器人协调策略，通过创造性的协调策略，包括锚定，牵引，倾斜，楔入，滑动，旋转和枢转产生协同效应的目的。为了建立相互的意识，多模态多速率融合算法的异构传感器将被设计用于定位，和时间同步算法将被开发以协调多机器人的行动。一个强化逻辑学习架构，利用“知识网络”，以加快学习，并结合一阶逻辑约束将被设计为实现SRC战略。该项目有可能通过引入正式的策略表示和知识驱动学习的视角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，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.