Meszaros, Erica (University of Chicago), Chandarana, Meghan (Carnegie Mellon University), Trujillo, Anna (NASA Langley Research Center), Allen, Bonnie Danette (NASA Langley Research Center)

Speech-Based Natural Language Interface for UAV Trajectory Generation

Scheduled for presentation during the "Path Planning - I" (WeA2), Wednesday, June 14, 2017, 10:20−10:40, Salon AB

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

In recent years, natural language machine interfaces have become increasingly common. These interfaces allow for more intuitive communication with machines, reducing the complexity of interacting with these systems and enabling their use by non-expert users. Most of these natural language interfaces rely on speech, including such well-known devices as the iPhone's Siri application, Cortana, Amazon's Alexa and Echo devices, and others. Given their intuitive functionality, natural language interfaces have also been investigated as a method for controlling unmanned aerial vehicles (UAVs), allowing non-subject matter experts to use these tools in their scientific pursuits. This paper examines a speech-based natural language interface for defining UAV trajectories. To determine the efficacy of this interface, a user study is also presented that examines how users perform with this interface compared to a traditional mouse-based interface. The results of the user study are described in order to show how accurately users were able to define trajectories as well as user preference for using the speech-based system both before and after participating in the user study. Additional data are presented on whether users had previous experience with speech-based interfaces and how long they spent training with the interface before participating in the study. The user study demonstrates the potential of speech-based interfaces for UAV trajectory generation and suggests methods for future improvement and incorporation of natural language interfaces for UAV pilots.