A Hasty Grid S&R Prototype Using Autonomous UTM and AI-Based Mission Coordination

Abstract

Search and Rescue (S&R) is a complex and critical problem. Generally, it is essential to find a missing person in the first 72 hours and if injured even sooner, since 86 % of battlefield deaths happen a half-hour after injury. Consequently, federal, state, local governments, and industry are highly interested in quickly finding missing persons, injured soldiers and pilots, or disaster victims in hostile environments. We offer a new S&R approach based on the use of autonomous drones or unmanned aircraft systems (UAS) Traffic Management (UTM) and melding two competing approaches, Hasty and Grid S&R. Our results demonstrate the feasibility of this approach in realistic simulated environments with varying wind, sensor visibility, obstacle density, terrain roughness, and battery levels. Further, we built working prototypes that allowed our MATLAB UTM simulation to interact with ArduPilot Software-In-The-Loop simulated drones and real hardware drones that recognized targets using convolutional neural networks (CNN). Also, these prototypes demonstrated the ability to identify targets and to coordinate S&R across multiple drones.