DRL for UGV Navigation in Crowded Environments
Research Paper#Robotics, Reinforcement Learning, Autonomous Navigation🔬 Research|Analyzed: Jan 3, 2026 17:16•
Published: Dec 30, 2025 15:17
•1 min read
•ArXivAnalysis
This paper addresses the limitations of existing DRL-based UGV navigation methods by incorporating temporal context and adaptive multi-modal fusion. The use of temporal graph attention and hierarchical fusion is a novel approach to improve performance in crowded environments. The real-world implementation adds significant value.
Key Takeaways
- •Proposes a DRL-based navigation framework (DRL-TH) for UGVs.
- •Utilizes temporal graph attention (TG-GAT) to capture temporal context.
- •Employs a graph hierarchical abstraction module (GHAM) for multi-modal fusion.
- •Demonstrates superior performance compared to existing methods in simulations.
- •Successfully implemented and tested on a real UGV.
Reference / Citation
View Original"DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios."