Resilient, UAV-embedded real-time computing
In this paper, we propose a hierarchical computational system architecture to support the target domain of realtime mobile computing in the context of unmanned aerial vehicles (UAVs). The overall architectural vision includes support for system resilience in the presence of uncertainties in the operational environment of surveillance UAVs. We report measurement-based results that are obtained from a UAV proxy demonstration apparatus. The apparatus consists of a Raspberry Pi (RPi) board that serves as an on-board UAV computer, working with support from a laptop that serves as the on-ground computing infrastructure where an operator "consumes" video information received from the UAV. We quantify the gap between the on-board UAV camera frame rate (input) and the on-ground operator-observed frame rate (output) for a specialized class of computer vision applications germane to the UAV-based aerial surveillance domain. The goal is to keep the frame rate observed by the ground operator as close (or ideally equal) to the on-board UAV camera frame rate (i.e. to preserve the real-time aspect) despite the unstable bandwidth availability in the channel connecting both ends. The proposed hierarchical approach significantly outperforms two considered baselines: one in which computation takes place entirely on the UAV computer and another in which computation takes place entirely on the ground. This improved performance is due to a more balanced resource sharing between the on-board UAV computer and UAV-to-ground communication channel. Later, we show how the observed frame rate improves when the RPi board is replaced with an NVIDIA Jetson TK1 board. Based on the observations gleaned from these "proxy" experiments, we sketch the fundamentals of our ongoing work in model-based predictive analysis of resilient "UAV swarm" computational architectures of the future.