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

RI-Small：在衰落环境中运行的机器人网络中的通信和运动规划集成框架

• 批准号: 0812338
• 负责人: Yasamin Mostofi
• 金额: $ 35万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812338&HistoricalAwards=false
• 关键词: RI; Small; Integrative; Framework; Communication

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.

该项目的目标是构建一个设计框架，以实现现实通信环境中移动协作机器人路由器网络的强大功能。 大多数有关移动协作决策的现有工作都使用过于简化的通信链路模型，假设任何两个代理之间存在完美链路或没有链路。此类模型不包含衰落、阴影和路径损耗等现实无线通信效应，因此不适合机器人路由器网络的稳健运行。 我们提出了新颖的运动规划策略，其中代理不断地重新配置自己，以通过网络最佳地路由信息。此外，理论结果和算法将在执行各种任务的实际自动驾驶车辆上实现。通过考虑现实通信的不可靠性，我们提出的框架将导致网络中信息的稳健流动。紧急响应、安全和监视是依赖于恶劣条件下自主网络的稳健和智能操作的应用示例，并且可以从拟议的工作中受益匪浅。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。