NRI: Collaborative Research: Robotics 2.0 for Disaster Response and Relief Operations

该项目开发和测试了新的压缩感知和传感器定位技术，这些技术适用于无数不同的移动机器人设计，同时可在当今的无线通信基础设施上运行。独特的原位实验室和现场实验为科学家和其他利益相关者提供了切实的结果，可以利用这些结果将这些系统推进到未来的现实世界危害管理场景中。研究小组开发了新的技术方法，结果是动员更多智能、自动化的“地面眼睛和耳朵”。外联工作包括：(i)将活动与从业人员结合起来；向环境工程师和急救人员等听众举办研讨会/网络广播；每年举办技术日营，吸引代表性不足群体的中学生学习工程；向当地K-12机构进行示范；将项目主题列入经常课程；国际合作。本项目引入了Robotics 2.0；这是一个针对自动机器人的框架，这些机器人是同事和共同保护者，适应并与人类一起工作。研究小组开发了一个网络控制网络（CCN），通过动态连接驱动、传感和控制子网络，允许多个固定和移动机器人环境传感和测量快速适应不断变化的环境。这种CCN控制软件和压缩感知架构的设计可以适用于灾害响应、缓解和管理以外的其他大规模问题，例如电网监控和重构，或应对交通拥堵和事故的区域城市交通运营。机器人传感平台不需要他们监测的危险和动态变化环境的先验知识。机器人2.0框架允许快速响应、准备和管理各种类型的灾难。

The project develops and tests novel compressive sensing and sensor locating techniques that are adaptable to a myriad of different mobile robot designs while operable on today's wireless communication infrastructures. Unique in-situ laboratory and field experiments provide tangible results to scientists and other stakeholders that can be leveraged to advance these systems into future real-world hazard management scenarios. The research team develops new technological approaches that results in mobilizing more intelligent, automated "eyes and ears on the ground." Outreach efforts include: (i) integration of the activities with practitioners; (ii) Seminars/webcasts to audiences like environmental engineers and first responders; (iii) Annual technology day camps to attract middle-schoolers from under-represented groups to engineering; (iv) Demonstrations to local K-12 institutions; (v) Inclusion of the project themes to the regular curricula; and (vi) International collaborations.This project introduces Robotics 2.0; a framework that targets autonomous robots that are co-workers and co-protectors, adapting to and working with humans. The research team develops a Cyber-Control Network (CCN) to allow multiple fixed and mobile robotic environmental sensing and measurements to adapt quickly to the changing environment by dynamically linking sub-networks of actuation, sensing, and control together. The design of such CCN ControlWare, and compressive sensing architectures, could be adapted to other large-scale problems beyond disaster response, mitigation, and management, such as power grid monitoring and reconfiguration, or regional urban traffic operations to respond to traffic congestion and incidents. The robotic sensing platforms do not require a-priori knowledge of the hazardous and dynamically changing environments they are monitoring. The Robotics 2.0 framework allows to swiftly respond, to prepare, and to manage various types of disasters.