Multi-UAV Situational Awareness via Distributed and Approximate Computing Techniques

Abstract

Recently, much progress has been made in using Neural Networks (NNs) for important yet narrowly focused tasks such as image classification (e.g., VGG-Net, ResNet), playing complex games like GO or other Computer Vision (CV) tasks. While these achievements are impressive, they are either achieved on computers with virtually unlimited resources or with little regard to real-time actionability. In this paper, we propose to combine the ubiquity of low-resource mobile devices, e.g., drones, with approximate- and distributed-computing techniques in order to make these NN techniques deployable on resource-constrained devices as well as to provide realtime information about the environment. We target situational awareness, which involves sensing the crucial factors in a new environment on a real-time basis. Specifically, we introduce intelligence to a team of drones in the form of real-time detection of a suspect/weapon using local resources and suspect identification in an emergency situation. We validate our proposed methods using Microsoft AirSim simulator via both simulations and hardware-in-the-loop emulations.