STTR Phase I: Video Streaming in a Robot MANET - Optimizing Throughput and Power Consumption via Adaptive Robot Re-positioning and Bandwidth Allocation

STTR 第一阶段：机器人 MANET 中的视频流 - 通过自适应机器人重新定位和带宽分配优化吞吐量和功耗

• 批准号: 0637667
• 负责人: Mike Perkins
• 金额: $ 15万
• 依托单位: Cardinal Peak, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0637667&HistoricalAwards=false
• 关键词: STTR; Phase; Video; Streaming; Robot

This Small Business Technology Transfer (STTR) Phase I Project investigates the streaming of video through a cluster of mobile ad hoc network (MANET) autonomous robots operating in a challenging multi-path propagation environment. This project will quantify the gains in video throughput and power consumption achievable by automatic robot re-positioning with respect to these initial positions. This research is motivated by the fact that, in multi-path and fading environments, small changes in a robot's location can lead to significant gains in the received signal strength. Automatic iterative optimization of the robot positions could therefore lead to robot constellations that achieve significantly higher throughput or require significantly less power. Classical approaches for addressing multi-path degradations include robust modulation techniques, such as orthogonal frequency-division multiplexing (OFDM), and spatial diversity. This project will determine if an OFDM-based 802.11g system, can achieve significant performance improvements when combined with an automatic robot re-positioning algorithm.Teams of robots are ideally suited for investigating environments intrinsically hostile to humans. Such environments arise in a variety of situations, including, in mines after an accident, in urban areas after a natural catastrophe, in buildings after a hazardous materials release, and in military operations. Additionally, in various video surveillance and security applications it will ultimately be cheaper to employ networks of mobile robots than to hire teams of security guards. Because humans rely disproportionately on vision for sensing their environment, the ability to stream video through MANET robot is essential. From a technology perspective, automatic cooperative re-positioning of the clusters nodes is a new dimension to explore with respect to optimizing a network's performance. The results of this project will assist in determining whether re-positioning algorithms should be incorporated in the design of future robot clusters, and if so, how such algorithms should operate, and how dynamic bandwidth allocation schemes can exploit the gains they enable.

这个小企业技术转移（STTR）第一阶段项目调查的视频流通过一个集群的移动的ad hoc网络（MANET）自主机器人在一个具有挑战性的多路径传播环境中运行。该项目将量化自动机器人重新定位相对于这些初始位置可实现的视频吞吐量和功耗的增益。这项研究的动机是，在多径和衰落的环境中，在机器人的位置的微小变化可以导致接收信号强度的显着增益的事实。因此，机器人位置的自动迭代优化可以导致实现显著更高的吞吐量或需要显著更少的功率的机器人星座。用于解决多径劣化的经典方法包括鲁棒调制技术，诸如正交频分复用（OFDM）和空间分集。该项目将确定一个基于OFDM的802.11g系统，是否可以实现显着的性能改善时，结合自动机器人重新定位算法。机器人团队非常适合调查环境本质上对人类不利。这种环境出现在各种情况下，包括在事故后的矿井中，在自然灾害后的城市地区，在危险材料释放后的建筑物中，以及在军事行动中。此外，在各种视频监控和安全应用中，采用移动的机器人网络最终将比雇用保安团队更便宜。由于人类不成比例地依赖视觉来感知环境，因此通过MANET机器人传输视频的能力至关重要。从技术的角度来看，自动合作重新定位的集群节点是一个新的维度来探索优化网络的性能。该项目的结果将有助于确定是否应将重新定位算法纳入未来机器人集群的设计中，如果是的话，这些算法应如何操作，以及动态带宽分配方案如何利用它们带来的收益。