LLM - Driven Adaptive Autonomous Robot Navigation via Multimodal Fusion for Diverse Environments

This paper presents a novel autonomous navigation framework that integrates Large Language Models (LLMs) with multimodal sensor fusion to enable dynamic obstacle avoidance and human-aware path planning in diverse environments. The proposed system leverages an FPGA-accelerated fusion pipeline, combining LiDAR and vision data for real-time perception. A Hungarian algorithm-based object matching technique ensures robust tracking, while a bird's-eye view (BEV) representation enhances spatial reasoning and occlusion handling. The fused sensory inputs are processed by a fine-tuned LLM, which contextualizes pedestrian behavior and environmental constraints to generate adaptive, human-centric navigation strategies. Unlike traditional rule-based methods, LLMs provide generalization capabilities to novel scenarios, significantly improving interaction with vulnerable pedestrians such as children, elderly individuals, and wheelchair users. Extensive evaluations in both simulated and real-world scenarios confirm the system's ability to reduce collisions and enhance navigation efficiency in high-density environments. By bridging semantic reasoning and robotic control, this work lays the foundation for next-generation intelligent navigation systems that are both safety-aware and scalable across autonomous platforms.