RI-Small: An Integrative Framework for Communication and Motion-planning in Robotic Networks Operating in Fading Environments

该项目的目标是为现实通信环境中移动协作机器人路由器网络的鲁棒功能构建一个设计框架。现有的关于移动协同决策的研究大多采用了过于简化的通信链路模型，假设两个智能体之间存在完美的链路或者没有链路。这样的模型不包含现实的无线通信效果，如衰落、阴影和路径损失，因此不适合机器人路由器网络的鲁棒运行。我们提出了新的运动规划策略，其中智能体不断重新配置自己，以最优地通过网络路由信息。此外，理论结果和算法将在从事广泛任务的实际自动驾驶汽车上实施。考虑到实际的通信不可靠性，我们所提出的框架将使网络中的信息流具有鲁棒性。应急响应、安全和监控是依赖于苛刻条件下自主网络的强大和智能运行的应用示例，并且可以从拟议的工作中获得巨大收益。该项目还通过在新墨西哥大学组织学习夏令营，对阿尔伯克基的女高中生产生了重大的教育影响。

The objective of this project is to build a design framework for robust functionality of mobile cooperative robotic router networks in realistic communication environments. Most of the existing work on mobile cooperative decision-making uses oversimplified models for communication links by assuming either a perfect link or no link between any two agents. Such models do not embrace realistic wireless communication effects such as fading, shadowing and path loss and therefore will not be suitable for robust operation of robotic router networks. We propose novel motion-planning strategies where the agents constantly reconfigure themselves to optimally route the information through the network. In addition, the theoretical results and algorithms will be implemented on actual autonomous vehicles engaged in a wide range of tasks. By considering realistic communication unreliability, our proposed framework will result in the robust flow of information in the network. Emergency response, security and surveillance are examples of applications that rely on robust and intelligent operation of autonomous networks in harsh conditions and can benefit tremendously from the proposed work. This project also has a significant educational impact on the female high school students of Albuquerque by organizing learning summer camps at the University of New Mexico.