VOICE CONTROL SYSTEM FOR PILOTING DRONES USING ARTIFICIAL INTELLIGENCE TECHNIQUES
DOI:
https://doi.org/10.52326/jes.utm.2026.33(2).01Keywords:
accessibility, voice control, human-machine interaction, BiLSTM, CNN, MFCC, neural networks, embedded systems, TinyMLAbstract
Voice control of drones is an innovation direction with high potential for many professional and social domains. This article analyzes the practical usefulness of a voice control system for drones based on an artificial intelligence model that runs locally, without an internet connection. The hybrid CNN+BiLSTM model is trained to recognize commands in Romanian and is deployed on the ESP-Drone platform with an ESP32-S2 microcontroller. Unlike commercial alternatives based on cloud infrastructure, the proposed solution runs entirely locally, eliminating latency and dependence on connectivity. The application domains with the greatest impact are examined, such as industrial inspections, search and rescue operations and precision agriculture, while also highlighting an often overlooked category of beneficiaries: people with motor disabilities, for whom voice control offers the possibility to pilot drones independently, without using their hands. The experimental results demonstrate a test accuracy of 99.4% and an end-to-end latency of 210–350 ms, confirmed under real flight conditions.
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