Evaluation of pilot and quadcopter performance from open-loop mission-oriented flight testing

Abstract

Unmanned aircraft systems (UAS) have experienced tremendous growth through both commercial (i.e., toys and videography) and defense avenues. The rapid expansion, particularly in the consumer market, has outpaced regulatory bodies. Certification to commercially operate such vehicles currently requires the successful completion of a knowledge examination, without the need to physically operate a vehicle. The focus of the work presented herein is on quantifying the pilot and multi-rotor performance in an attempt to provide quantitative metrics that can be used to establish training and certification for pilots and aircraft. Test pilots were categorized based on their experience level, and the quadrotor unmanned aircraft was categorized based on the flight control mode. Cross-track command (CTC) and path error (PE) were calculated as potential time-domain metrics to quantify pilot and quadcopter performance. Individual binary logistic regression models were developed to identify the pilot experience level (PEL) and UAV control level (UCL) from the decision tree outcomes. A verification test case was included to evaluate the established regression models. Results show that the models can evaluate pilot and quadcopter performance individually, which can be used to develop the pilot training curriculum and/or evaluate pilot effectiveness in specific flight scenarios.