Voice-controlled autonomous navigation for smart wheelchairs using ROS-based SLAM

Abstract

Abstract Smart wheelchairs have the potential to significantly improve autonomy for individuals with severe motor impairments, yet existing systems often exhibit limited speech robustness, insufficient handling of dynamic environments, and a lack of rigorously validated safety mechanisms. This work presents a fully integrated, voice-controlled smart wheelchair that advances assistive mobility through three main contributions. First, we introduce an inclusive speech-recognition module built from a fine-tuned deep learning model trained on a custom dataset that incorporates recordings from users with mild speech impairments. This adaptation improves robustness to non-standard pronunciation and maintains reliable command execution under realistic noise conditions (70–75 dB), achieving a Word Error Rate of 6.7% in quiet environments. Second, rather than proposing a new SLAM method, we develop a system-level navigation framework that optimally integrates 2D LiDAR-based SLAM (GMapping), AMCL localization, and a dual-level voice-command interface within a real-time coordination layer. This includes a quantitatively parameterized safety module featuring adaptive speed modulation and experimentally calibrated emergency-stop thresholds, ensuring reliable operation in dynamic indoor environments. Third, we conduct an extensive experimental campaign in both simulation and real-world conditions to provide a reproducible and quantitative evaluation of system performance. Tests involving dynamic obstacles (pedestrians, wheeled carts, small animals), constrained passages, and diverse acoustic settings demonstrate a mean localization error below 10 cm, a 94% goal-completion rate, and an end-to-end voice-to-motion latency of 0.8 s. Together, these contributions provide a low-cost, experimentally validated assistive-mobility platform that emphasizes inclusive voice interaction, robust real-time navigation, and safety-aware behavior. The proposed framework moves beyond component-level studies by offering a coherent, deployable, and reproducible solution for everyday indoor environments.