融合强化学习与动态窗口法，实现高自由度机器人在复杂环境中的自适应3D导航
3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
- 用强化学习结合动态窗口法，根据稀疏点云实时调整机器人的运动与形变
- 1080次仿真测试显示，该方法变形能力更强，路径完成率接近100%
- 适合需在有限感知条件下自主导航的微型机器人系统研究者参考
本文提出一种新型混合方法，将强化学习（RL）与动态窗口法（DWA）结合，用于高自由度机器人在复杂受限环境中的自适应3D局部导航。该方法利用稀疏点云数据，动态调节可变形微机器人的运动与形状，使其在复杂环境中向目标前进的同时最大化占据体积。我们在模拟血管网络中评估该框架，基于1080次试验的结果表明，将强化学习与基于DWA的局部规划器结合，显著提升了形变与导航性能，优于纯强化学习和模型基方法。所提出的自主控制器在训练过程中持续实现高形变与近乎完美的路径完成率，并在未见过的场景中保持稳健表现。这些发现凸显了混合规划策略在稀疏感知条件下的高效性与适应性。
原文摘要 · Abstract (English)
In this paper, we present a novel hybrid approach that combines Reinforcement Learning (RL) with Dynamic Window Approach (DWA) for adaptive 3D local navigation of high-degree-of-freedom robotic systems. Our method leverages sparse point cloud data to dynamically adjust both the motion and the shape of a deformable microrobot, enabling the system to navigate toward a goal in complex, constrained environments while maximizing the occupied volume. We evaluate our framework in a simulated vascular network. Experimental results, based on 1080 trials, indicate that integrating RL with a DWA-based local planner significantly enhances both deformation and navigation capabilities compared to pure RL and model-based methods. In particular, the proposed autonomous controller consistently achieves high deformation and near-perfect path completion during training and maintains robust performance in unseen scenarios. These findings highlight the potential of hybrid planning strategies for efficient and adaptive 3D navigation under sparse sensory conditions.
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