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.

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When fly-by-wire controls first appeared, much research focused on how to evaluate control laws and bare airframes for airworthiness given the reliance on electronic augmentation. Similarly, handling qualities and methods of evaluation for novel flying vehicles have been discussed whenever novel designs are developed. In the last 20 years, with the proliferation of unmanned aircraft systems (UAS), much research has focused on transferring existing methods of performance evaluation and airworthiness assessment to accommodate the unique feature of many UAS. However, the changes in sensory information and pilot experience levels require examining the aircraft and pilots together in a holistic manner. In this work, the authors present the results of a broad study focused on evaluation of both pilot skill and aircraft performance in a holistic way. In this study, the skill of the remote pilot is considered as well as the effectiveness of the electronic flight control system because the evaluation is conducted on the system at-large as opposed to considering each piece individually. An evaluation approach using mission-task-elements (MTEs), like that used within Aeronautical Design Standard-33 Handling Qualities Requirements for Military Rotorcraft (ADS-33), is developed and presented for UAS. In the study, new UAS pilots were evaluated before, during, and after an intensive training program using qualitative measures (like Cooper–Harper rating) and quantitative measurements (like elapsed time) of their performance while flying the prescribed MTEs. The techniques described can provide insight into pilot performance, airframe airworthiness, and effectiveness of stability systems in an efficient test program and the results are inherently easy to understand in the context of MTEs.

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Human-piloted, remotely piloted, and autonomous aircraft are likely to share the air space in the near future. Furthermore, the density of air traffic is likely to increase as urban air mobility and air package delivery operations become more prevalent. This paper discusses a collision avoidance approach based on velocity potentials: it is scalable to large numbers of aircraft and is highly likely to be easily interpretable for human pilots, passengers, and bystanders. This paper describes the velocity potential approach for three dimensional collision avoidance and uses Monte Carlo simulations to examine (1) the effect of number of aircraft in the airspace; (2) the number of aircraft that are actively tracked for collision avoidance; (3) the elementary potentials used for collision avoidance.