CPS: TTP Option: Synergy: Collaborative Research: Dependable Multi-Robot Cooperative Tasking in Uncertain and Dynamic Environments

CPS：TTP 选项：协同：协作研究：不确定和动态环境中可靠的多机器人协作任务

• 批准号: 1446288
• 负责人: Hai Lin
• 金额: $ 90万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446288&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Synergy; Collaborative

Driven by both civilian and military applications, such as coordinated surveillance, search and rescue, underwater or space exploration, manipulation in hazardous environments, and rapid emergency response, cooperative actions by teams of robots has emerged as an important research area. However, the coordination strategies for such robot teams are still developed to a great extent by trial-and-error processes. Hence, the strategies cannot guarantee mission success. This award supports fundamental research to provide a provably correct formal design theory of multi-robot systems that guarantees mission success. Furthermore, results from the research can be extended to the design of more general cyber-physical systems (CPSs) consisting of distributed and coordinated subsystems, such as the national power grid, ground/air traffic networks, and manufacturing systems. These CPSs are critical components of the national civil infrastructure that must operate reliably to ensure public safety. The multidisciplinary approach taken will help broaden participation of underrepresented groups in research and positively impact engineering education. Focusing on multi-robot teams, the goal of the research is to build foundations for a provably correct formal design theory for CPSs. This design theory will guarantee a given global performance of multi-robot teams through designing local coordination rules and control laws. The basic idea is to decompose the team mission into individual subtasks such that the design can be reduced to a local synthesis problem for individual robots. Multidisciplinary approaches combining hybrid systems, supervisory control, regular inference and model checking will be utilized to achieve this goal. The developed theory will enable robots in the team to cooperatively learn their individual roles in a mission, and then automatically synthesize local supervisors to fulfill their subtasks. A salient feature of this method lies on its ability to handle environmental uncertainties and unmodeled dynamics, as there is no need for an explicit model of the transition dynamics of each agent/robot and their interactions with the environment. In addition, the design is online and reactive, enabling the robot team to adapt to changing environments and dynamic tasking. The derived theory will be implemented as software tools and will be demonstrated through real robotic systems consisting of unmanned ground and aerial vehicles in unstructured urban/rural areas.

在协同监视、搜救、水下或空间探索、危险环境操作和快速应急反应等民用和军用应用的推动下，机器人团队的合作行动已成为一个重要的研究领域。然而，这种机器人团队的协调策略在很大程度上仍然是通过试错过程来开发的。因此，这些战略不能保证任务成功。该奖项支持基础研究，以提供可证明正确的多机器人系统形式设计理论，确保任务成功。此外，研究结果还可以扩展到更通用的网络物理系统(CPSS)的设计，这些系统由分布式和协调的子系统组成，如国家电网、地面/空中交通网络和制造系统。这些CPSS是国家民用基础设施的重要组成部分，必须可靠地运行，以确保公共安全。所采取的多学科方法将有助于扩大代表性不足群体对研究的参与，并对工程教育产生积极影响。本研究以多机器人团队为研究对象，旨在为CPSS的形式化设计理论奠定基础。该设计理论通过设计局部协调规则和控制律，保证了多机器人团队给定的全局性能。其基本思想是将团队任务分解为单个子任务，使得设计可以归结为单个机器人的局部综合问题。将利用混合系统、监督控制、规则推理和模型检验相结合的多学科方法来实现这一目标。开发的理论将使团队中的机器人能够合作学习它们在任务中的个人角色，然后自动合成本地主管来完成他们的子任务。该方法的一个显著特点在于其处理环境不确定性和未建模动态的能力，因为不需要每个智能体/机器人的转换动力学以及它们与环境的相互作用的显式模型。此外，设计是在线的和反应性的，使机器人团队能够适应不断变化的环境和动态任务。衍生的理论将作为软件工具实施，并将通过由非结构化城市/农村地区的无人驾驶地面和飞行器组成的真实机器人系统进行演示。