Hybrid Motion Planning with DRL for Mobile Robot Navigation
Research Paper#Robotics, AI, Navigation, Reinforcement Learning🔬 Research|Analyzed: Jan 3, 2026 08:50•
Published: Dec 31, 2025 05:58
•1 min read
•ArXivAnalysis
This paper addresses a critical challenge in autonomous mobile robot navigation: balancing long-range planning with reactive collision avoidance and social awareness. The hybrid approach, combining graph-based planning with DRL, is a promising strategy to overcome the limitations of each individual method. The use of semantic information about surrounding agents to adjust safety margins is particularly noteworthy, as it enhances social compliance. The validation in a realistic simulation environment and the comparison with state-of-the-art methods strengthen the paper's contribution.
Key Takeaways
- •Proposes a hybrid approach (HMP-DRL) for mobile robot navigation, combining global path planning with local DRL.
- •Integrates checkpoints from the global planner into the DRL policy.
- •Employs an entity-aware reward structure for social compliance, adjusting safety margins based on agent types.
- •Demonstrates superior performance compared to state-of-the-art methods in simulations.
Reference / Citation
View Original"HMP-DRL consistently outperforms other methods, including state-of-the-art approaches, in terms of key metrics of robot navigation: success rate, collision rate, and time to reach the goal."