Design and implementation of voice-command controller for fixed-wing unmanned aerial vehicles using automatic speech recognition and natural language processing techniques

This paper explores the development of a voice command controller leveraging the capabilities of an automatic speech recognition (ASR) system and natural language processing (NLP) technique to manage a fixed-wing unmanned aerial vehicle (UAV). The controller is designed to interpret voice commands for controlling fixed-wing UAVs. The implementation of the system involved two key stages: (1) implementation of a voice command controller using integrated ASR and NLP techniques deployed in a simulated plane in the SITL simulator followed by (2) deployment of the controller to an actual Sky Surfer plane fixed-wing aircraft. The results indicate that the algorithm achieved an average confidence rate of 91.86 % in transcribing voice commands to words, with a Word Error Rate (WER) of approximately 0.021. The developed system demonstrated the ability to interpret both low-level and high-level commands for UAV control interfaces. Such an interface offers greater intuitiveness compared to traditional RC controls, potentially requiring less training to operate effectively. Moreover, it reduces human workload, as once commands are issued, the system can execute them without the need for continuous supervision.