Adaptive Deployment for Autonomous Agricultural UAV Swarms

Abstract

Unmanned aerial vehicles (UAV) play a critical role in many edge computing deployments and applications. UAV are prized for their maneuverability, low cost, and sensing capacity, facilitating many applications that would otherwise be prohibitively expensive or dangerous without them. UAV are cheaper than alternative aerial analysis methods, but still incur costs from expensive human piloting and workloads which necessitate high-resolution coverage of large areas. Recently, autonomous UAV swarms have emerged to increase the speed of deployments, decrease the cost and scope of human piloting, and improve the quality of autonomous decision-making through data sharing. Autonomous UAV deployments, however, suffer from external factors. UAV are inherently power-constrained, with low onboard battery lives and limited ability to siphon power from the edge systems that support them. Certain environmental conditions, like inclement weather, wind, extreme heat, and low light also affect UAV power consumption, sensed data quality, and ultimately mission success. In this paper, we present an empirically based model for efficient autonomous swarm deployment. We built and deployed a real autonomous UAV swarm to map leaf defoliation in soybeans. Using this deployment, we determined environmental conditions which led to malfunctions, inefficient edge energy usage, and mispredictions. Using these findings, we developed a deployment model for UAV swarms that decreases malfunctions and data irregularities by 4.9X and decreases edge energy consumption by 45%, while increasing deployment times by only 4%.