用引导点提升复杂环境下的导航安全与效率
NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points
- 引入引导点作为强化学习的定向提示，缓解感知误差影响
- 仿真与实测均显示成功率最高、路径最优化，且避障稳健
- 适合需要高鲁棒性导航的机器人系统开发者参考
导航系统中的运动规划极易受上游感知误差影响，尤其在行人检测与跟踪方面。为此，本文提出一种基于强化学习框架的新型方向性线索——引导点，并设计了结构化方法：包括障碍物边界提取、潜在引导点检测及冗余消除。为将引导点融入导航流程，提出感知到规划的映射策略，统一融合原始激光数据、行人检测与跟踪信息以及引导点，使强化学习智能体能有效利用三者间的互补关系。定性与定量仿真结果表明，该方法在成功率和近似最优行进时间上表现最佳，显著提升安全性和效率。此外，动态走廊与大厅的真实世界实验验证了机器人在复杂场景中自信绕障、稳定避让行人的能力。
原文摘要 · Abstract (English)
Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points--a novel directional cue within a reinforcement learning-based framework--is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot's ability to confidently navigate around obstacles and robustly avoid pedestrians.
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