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.

在平民和军事应用的驱动下，例如协调的监视，搜索和救援，水下或太空勘探，危险环境中的操纵以及快速的紧急响应，机器人团队的合作行动已成为重要的研究领域。但是，此类机器人团队的协调策略仍在很大程度上通过反复试验的流程制定。 因此，这些策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的正式设计理论，可确保任务成功。 此外，研究的结果可以扩展到由分布式和协调子系统（例如国家电力网格，地面/空中交通网络以及制造系统）组成的更通用的网络物理系统（CPSS）的设计。这些CPS是国家民用基础设施的关键组成部分，必须可靠地运作以确保公共安全。采用的多学科方法将有助于扩大代表性不足的群体在研究中的参与，并积极影响工程教育。研究专注于多机器人团队，该研究的目标是为CPSS的正式设计理论建立基础。该设计理论将通过设计当地协调规则和控制法律来确保多机器人团队的全球性能。基本思想是将团队任务分解为各个子任务，以便将设计简化为单个机器人的局部合成问题。将使用混合系统，监督控制，定期推理和模型检查的多学科方法来实现此目标。开发的理论将使团队中的机器人能够合作学习他们在任务中的个人角色，然后自动合成本地主管以实现其子任务。这种方法的一个显着特征在于它可以处理环境不确定性和未建模动态的能力，因为无需明确的每个代理/机器人的过渡动力学模型及其与环境的交互。此外，设计是在线和反应性的，使机器人团队能够适应不断变化的环境和动态任务。派生的理论将作为软件工具实施，并将通过由无人驾驶的地面和飞机在非结构化的城市/农村地区组成的实际机器人系统来证明。