CAREER: Multi-Agent Decision Making and Optimization using Communication as a Sensor

该项目的目标是实现机器人之间的协调和本地化，即使一些机器人的行为不可信。这种方法是使用通信信号，并控制一些机器人的运动，以一种可证明支持协调行为的方式来了解环境和其他代理。多智能体网络物理系统(CPS)将作为自动驾驶汽车、送货无人机和灾难应对机器人在社会上产生影响。该项目的结果将改善机器人平台的任务智能，对许多领域具有重要意义：从搜索和救援(SAR)任务，到立方体卫星和空间探索。该项目包括研究任务和教育活动之间的紧密结合，包括参与Hack for Humanity，在那里学生将探索SAR任务。这个项目将通过桥接机器人学和通信，推导出稳健和安全的上下文感知的算法基础，用于协调多代理CP。这些系统的一个核心支持能力是安全、强大、背景感知和协调。代理必须知道他们的状态和其他人的状态，以及如何最好地使用这些信息来协调和完成任务级目标。然而，尽管这种需求是普遍存在的，但合作伙伴关系本身并不是。因此，在跨不同平台获取这些关键信息方面存在技术障碍。将探索一种算法和数学框架，以1)结合CPS代理的通信和移动性控制，以实现将通信作为传感器来解锁与世界交互的新理解，以及2)捕捉这些信息对多代理任务的可靠性、稳健性和安全性的影响。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。

The goal of this project is to achieve coordination and localization among robots, even if some of the robots are behaving in an untrustworthy way. The approach is to use communication signals, and to control the motion of some robots, to learn about the environment and other agents in a way that provably supports coordinated behaviors. Multi-agent Cyber-Physical Systems (CPS) are poised for impact in society as self-driving cars, delivery drones, and disaster response robots. The results from this project will lead to improved mission intelligence for robotic platforms with significance across many areas: from Search and Rescue (SAR) tasks, to CubeSats and space exploration. The project includes tight integration between research tasks and educational activities, including participation in Hack for Humanity where students will explore SAR tasks.This project will derive the algorithmic foundations of robust and secure contextual awareness for coordination of multi-agent CPS by bridging robotics and communication. A core enabling capability for these systems is secure, robust, contextual awareness and coordination. Agents must know their state, others' states, and how best to use this information to coordinate and complete mission-level goals. However, while this need is universal, the CPS themselves are not. Thus, there exist technological barriers to obtaining this critical information across heterogeneous platforms. An algorithmic and mathematical framework will be explored to 1) combine communication and mobility control of CPS agents to enable communication-as-a-sensor that unlocks a new understanding of interactions with the world and 2) capture the impact of this information on reliability, robustness, and security of multi-agent tasks.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.