Robotic Router Formation in Realistic Communication Environments
Robotic Router Formation in Realistic Communication Environments
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DOI：
10.1109/tro.2012.2188163
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发表时间：
2012-08
影响因子：
7.8
通讯作者：
Yuan Yan;Yasamin Mostofi
中科院分区：
文献类型：
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作者：
Yuan Yan;Yasamin Mostofi
在本文中，我们考虑了机器人路由器形成问题，其中两个节点需要使用多个移动路由器来维持其在大范围内的连通性。我们对这类网络在自然经历路径损耗、阴影和多径衰落的现实通信环境中的稳健运行感兴趣。通过将我们先前提出的随机通道学习框架与机器人路由器优化相结合，我们提出了一种概率路由器形成和运动规划方法。我们进一步考虑了网络的功率限制，包括通信和运动成本，并描述了潜在的权衡。我们没有采用通常的通过最大化Fiedler特征值进行队形优化的方法，而是采用了不同的方法，并使用端到端误码率(BER)作为我们的性能衡量标准。我们表明，与仅考虑用于通信和/或最大化场特征值的磁盘模型相比，所提出的框架导致了不同的机器人配置，具有相当好的性能。最后，我们用一个简单的初步实验展示了性能，重点是定位误差的影响。沿着这条线，我们展示了本地化质量和渠道相关性/学习质量之间有趣的相互作用。
In this paper, we consider the problem of robotic router formation, where two nodes need to maintain their connectivity over a large area by using a number of mobile routers. We are interested in the robust operation of such networks in realistic communication environments that naturally experience path loss, shadowing, and multipath fading. We propose a probabilistic router formation and motion-planning approach by integrating our previously proposed stochastic channel learning framework with robotic router optimization. We furthermore consider power constraints of the network, including both communication and motion costs, and characterize the underlying tradeoffs. Instead of taking the common approach of formation optimization through maximization of the Fiedler eigenvalue, we take a different approach and use the end-to-end bit error rate (BER) as our performance metric. We show that the proposed framework results in a different robotic configuration, with a considerably better performance, as compared with only considering disk models for communication and/or maximizing the Fielder eigenvalue. Finally, we show the performance with a simple preliminary experiment, with an emphasis on the impact of localization errors. Along this line, we show interesting interplays between the localization quality and the channel correlation/learning quality.