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

这个项目的目标是建立一个强大的功能的移动的合作机器人路由器网络在现实的通信环境中的设计框架。 大多数现有的工作移动的合作决策使用过度简化的模型，假设任何两个代理之间的完美链接或没有链接的通信链路。这样的模型不包括现实的无线通信效果，如衰落，阴影和路径损耗，因此将不适合机器人路由器网络的鲁棒操作。 我们提出了新的运动规划策略，其中代理不断重新配置自己，以最佳方式通过网络的信息。此外，理论结果和算法将在从事各种任务的实际自动驾驶车辆上实施。通过考虑现实的通信不可靠性，我们提出的框架将导致在网络中的强大的信息流。应急响应、安全和监控是依赖于在恶劣条件下自主网络的稳健和智能操作的应用的例子，可以从拟议的工作中受益匪浅。该项目还通过在新墨西哥州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.