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

这个小型企业技术转移(STTR)第一阶段项目研究通过移动自组织网络(MANET)自主机器人集群在具有挑战性的多路径传播环境中运行的视频流。该项目将量化通过机器人自动重新定位相对于这些初始位置可实现的视频吞吐量和功率消耗方面的收益。这项研究的动机是，在多径和衰落环境中，机器人位置的微小变化可以导致接收信号强度的显著增加。因此，机器人位置的自动迭代优化可以导致机器人星座实现显著更高的吞吐量或需要显著更低的功率。解决多径退化的经典方法包括稳健的调制技术，例如正交频分多路复用(Ofdm)和空间分集。该项目将确定基于ofdm的802.11g系统与自动机器人重新定位算法相结合时是否可以实现显著的性能改进。机器人团队非常适合于调查天生对人类怀有敌意的环境。这种环境在各种情况下都会出现，包括事故后的矿井、自然灾害后的城市地区、危险物质释放后的建筑物和军事行动。此外，在各种视频监控和安全应用中，最终使用移动机器人网络比雇佣保安团队更便宜。由于人类不成比例地依赖视觉来感知他们的环境，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，集群节点的自动协作重新定位是优化网络性能方面需要探索的新维度。该项目的结果将有助于确定重新定位算法是否应该被纳入未来机器人集群的设计中，如果是的话，这些算法应该如何操作，以及动态带宽分配方案如何利用它们所带来的收益。

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.