mlsanityData to 14 Oct 2026

Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles

arXiv 2503.01127 ↗First posted 3 Mar 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Distance-based reward mechanisms in deep reinforcement learning (DRL) navigation systems suffer from critical safety limitations in dynamic environments, frequently resulting in collisions when visibility is restricted. We propose DRL-NSUO, a novel navigation strategy for unexpected obstacles that leverages the rate of change in LiDAR data as a dynamic environmental perception element. Our approach incorporates a composite reward function with environmental change rate constraints and dynamically adjusted weights through curriculum learning, enabling robots to autonomously balance between path efficiency and safety maximization. We enhance sensitivity to nearby obstacles by implementing short-range feature preprocessing of LiDAR data. Experimental results demonstrate that this method significantly improves both robot and pedestrian safety in complex scenarios compared to traditional DRL-based methods. When evaluated on the BARN navigation dataset, our method achieved superior performance with success rates of 94.0% at 0.5 m/s and 91.0% at 1.0 m/s, outperforming conservative obstacle expansion strategies. These results validate DRL-NSUO's enhanced practicality and safety for human-robot collaborative environments, including intelligent logistics applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 25 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

11 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments18 Oct

Other references · 10

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 49
  3. 2023DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstaclescited by 37
  4. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
  5. 2020Benchmarking Metric Ground Navigationcited by 20
  6. 2023Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learningcited by 11
  7. 2021IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learningcited by 10
  8. 2022Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shapingcited by 10
  9. 2022Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022cited by 4
  10. 2023Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 2nd BARN Challenge at ICRA 2023cited by 3

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  1. 2024Deep Reinforcement Learning-based Large-scale Robot Exploration
  2. 2026DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
  3. 2024Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control
  4. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  5. 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
  6. 2024Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space
  7. 2025DRL-TH: Jointly Utilizing Temporal Graph Attention and Hierarchical Fusion for UGV Navigation in Crowded Environments
  8. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning