This research addresses the limitations of traditional visual and verbal navigation (VLN) tasks by introducing an innovative RFID-based, brain-inspired route correction scheme. This system integrates visual, voice, RFID, and odometer sensors to tackle real-world navigation challenges such as lighting changes and hardware errors. By combining the brain’s grid coding and spatial perception mechanisms with RFID technology, we achieve precise location tracking. Additionally, a PID controller simulates the brain’s perception of spatial edges, providing real-time deviation correction for the agent. Moreover, a reinforcement learning-based path planning method, enhanced with domain randomization, ensures continuous navigation. This approach, validated through VLN simulations, significantly improves navigation accuracy and success rates without incurring high learning costs, marking substantial progress in robot navigation technology.