Development of a Cost-effective On-device Natural Language Command Navigation System for Mobile Robots in Challenging Indoor Scenarios

Abstract

The increasing demand for mobile robots in indoor environments such as hospitals, offices, and residential buildings has highlighted the need for affordable, privacy-preserving navigation and interaction capabilities. This study introduces a cost-effective, on-device BERT-based natural language navigation system that enables robots to interpret human commands into goals. The system is designed for deployment on lightweight embedded computers and updates without requiring model retraining, ensuring scalability and flexibility. We also propose an AprilTAg-augmented SLAM system to reduce navigation errors in common indoor challenges like ramps and transparent obstacles. Experiments in real-world settings statistically demonstrate that our solution significantly reduces errors in these scenarios, offering a more reliable approach to indoor robot navigation.