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工作空间更具挑战性的地点，包括陡峭和破碎的地形、地下洞穴和其他极端地点。这些任务往往超过单个机器人的机动性和感知能力，如武力和有效载荷能力。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有力的交互。为了具体解决多机器人在具有挑战性的地点的协作问题，该项目将开发一种高效的协调机制，用于在未知、动态或苛刻的环境中进行协作移动操作。本项目有可能将研究人员的研究范围从灵巧的多指操作转变为基于协作移动机器人团队的移动操作。此外，该项目将促进研究人员所在机构的研究基础设施和能力，转型方法可以扩展到NASA和当地公司感兴趣的一般问题。这个研究基础设施改进Track-4 EPSCoR研究人员项目将为威奇托州立大学的一名助理教授提供奖学金，并为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，使合作战略中能够正式表示、学习和规划，以完成具有物理挑战性的机器人任务。该框架的特点是多机器人的紧耦合和共生协调策略，目的是通过创造性的协调策略产生协同效果，包括锚定、拖曳、倾斜、楔入、滑动、旋转和旋转。为了建立相互感知，将设计一种针对不同传感器的多通道多速率融合算法进行定位，并开发时间同步算法来协调多机器人的动作。将设计一种强化逻辑学习体系，利用“知识网络”加速学习，并纳入一阶逻辑约束，以实施SRC策略。该项目有可能通过引入正式战略表示和知识驱动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。

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.