Autocomplete of 3D Motions for UAV Teleoperation

Tele-operating aerial vehicles without any automated assistance is challenging due to various limitations, especially for inexperienced users. Autocomplete addresses this problem by automatically identifying and completing the user's intended motion. Such a framework uses machine learning to recognize and classify human inputs as one of a set of motion primitives, and then, if the human operator accepts, synthesizes the motion in order to complete the desired motion. This has been shown to improve the performance of the system and reduce operator workload. Previous Autocomplete systems focused on different 2D motions (line, arc, sine,..). However, since most UAVs tasks are in a 3D world, this paper introduces 3D Autocomplete for 3D motions. Moreover, the proposed framework presents just-in-time prediction of the 3D motions by proposing a change point detection technique, which allows the framework to autonomously identify when to conduct a prediction. Also, it deals with variable motion sizes. Real time simulation results show that the proposed framework is capable of predicting the user intentions after change point detection.