The Impact of Different Levels of Autonomy and Training on Operators’ Drone Control Strategies

Abstract

Unmanned Aerial Vehicles (UAVs), also known as drones, have extensive applications in civilian rescue and military surveillance realms. A common drone control scheme among such applications is human supervisory control, in which human operators remotely navigate drones and direct them to conduct high-level tasks. However, different levels of autonomy in the control system and different operator training processes may affect operators’ performance in task success rate and efficiency. An experiment was designed and conducted to investigate such potential impacts. The results showed us that a dedicated supervisory drone control interface tended toward increased operator successful task completion as compared to an enhanced teleoperation control interface, although this difference was not statistically significant. In addition, using Hidden Markov Models, operator behavior models were developed to further study the impact of operators’ drone control strategies as a function of differing levels of autonomy. These models revealed that people with both supervisory and enhanced teleoperation control training were not able to determine the right control action at the right time to the same degree that people with just training in the supervisory control mode. Future work is needed to determine how trust plays a role in such settings.