Description

<p><strong>In military operations, large remotely piloted UAVs have been successfully deployed for several years. The success in this application has spawned a new area of research - micro-autonomous aerial vehicles (micro-AAVs). Over the past two years, this research area has been exploited by universities, and has resulted in a rich collection of micro-AAVs platforms which range from the small, open-platform system using open source waypoint navigation software; to small, production ready, commercial-off-the-shelf platforms with complex highly intelligent flight management systems. These platforms are capable of supporting a full array of sensors and cameras ranging from high-resolution, true-color, still images to high-resolution real-time video streams. In addition, some platforms are capable of supporting near infrared (NIR) cameras that can be used for Normalized Difference Vegetation Index (NDVI) data products useful for vegetation health monitoring similar to those generated today by our team using Moderate </strong></p><p><strong>Resolution Imaging Spectroradiometer (MODIS) satellite data.</strong></p><p><strong>Additionally, for over a decade now, rovers have been successfully used on Mars to collect terrestrial close-up imagery and other sensor data. For future lunar and planetary exploratory missions, the development of smaller and more efficient micro-rover platforms have been proposed, and have been prototyped in a variety of forms and locomotive means. For successful and safe exploration of these surfaces, ultra-high resolution terrain and feature data, as well as, a flexible autonomous system to gather and process this data over wide areas will be required.</strong></p><p><strong>For this project, the potential of simulating a rover-balloon tethered system,</strong> <strong>autonomous cloud enabled system</strong><strong>,</strong><strong> for gathering and processing low altitude high resolution imagery for the purposes of terrain model and thematic data product creation was explored, and demonstrated. The tablet cameras and sensors were used as a proxy for the AAV sensor and image data.  A typical limiting factor associated with the small payload of these systems (micro-AAVs) is the computational power that can be deployed on them, which, correspondingly, limits their autonomous capabilities. To increase computational capacity, data was pushed to a cloud location for access by the processing system. Therefore, this project explored using cloud computing to increase its computational capacity on a tablet.  </strong></p><p><strong>The tablet and commercial off the shelf (COTS) smartphone with camera was able to establish communication with the cloud by tethering to a tablet mobile Wi-Fi hotspot for internet access.  The tablet allowed for real-time data processing, analysis, and autonomous flight operations based on those observations. </strong></p><p><strong>Therefore, for this project, the effective computational power of these platforms was increased by simulating cloud computing services via a local virtual machine data processing system. Using this Virtual Machine to establish communication with the cloud, the computational capacity of the simulated micro-AAV was augmented and enabled real-time data processing and analysis based on those observations.</strong></p><p><strong>Future testing of this data processing flow via a virtual machine could be directly translated to current cloud computing services with little modification, and once implemented could enhance available UAV aerial rapid response platforms capabilities in their ability to respond to natural or manmade disasters.</strong></p>

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