Visual Communication with UAS: Estimating Parameters for Gestural Transmission of Task Descriptions

Visual communication, and especially that based on gestures, can be an alternative to radio-based command and control links to small unmanned aerial vehicles (UAVs). In order to allow this natural form of communication to be an equivalent substitute to a conventional data link with regard to the efficient transfer of task data, the human-UAV-interaction must consist of as few gestural parameters as possible to keep the mental load for the user low, but at the same time provide enough information for the UAV to estimate the users intention. For this purpose, an experimental study under laboratory conditions with N = 20 German-speaking participants has been conducted to generate a dataset with 120 verbal formulations for six common UAV related tasks. The collected data has been evaluated regarding the distribution of sentences, words and parts of speech. Results indicate that users prefer short task descriptions prioritizing verbs and nouns (84.15%) with a mean number of 2.55 (SD = 1.57) words per sentence. All tasks considered were described with an average of 1.73 sentences (SD = 1.00). A slot filling approach is presented to map parameters in user intents.