用激光数据变化率提升机器人避障能力，让导航更安全。
Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles
- 以激光数据变化率作为动态感知信号，增强对突发障碍物的敏感度。
- 在BARN数据集上，0.5米/秒时成功率达94%，1.0米/秒时达91%。
- 适合智能物流等需人机协同的复杂动态场景使用。
基于距离的奖励机制在深度强化学习（DRL）导航系统中，在动态环境中存在严重安全隐患，尤其在可视范围受限时易引发碰撞。本文提出DRL-NSUO，一种应对突发障碍物的新导航策略，利用激光雷达（LiDAR）数据的变化率作为动态环境感知要素。该方法采用复合奖励函数，引入环境变化率约束，并通过课程学习动态调整权重，使机器人能自主平衡路径效率与安全性。通过短距离特征预处理提升对近距障碍物的敏感性。实验表明，该方法在复杂场景下显著提升了机器人和行人的安全性。在BARN导航数据集上，速度为0.5米/秒时成功率高达94.0%，1.0米/秒时为91.0%，优于保守的障碍物扩展策略。结果验证了DRL-NSUO在人机协作环境中的实用性和安全性，适用于智能物流等应用场景。
原文摘要 · Abstract (English)
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.
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