用学习方法提升室内机器人的导航能力，兼顾全局路径与局部避障。
Learning-Based Navigation for Indoor Mobile Robots
- 用监督学习训练全局路径规划器，结合专家轨迹优化
- 局部规划器经行为克隆和PPO强化学习双重优化，提升避障可靠性
- 在仿真与真实场景均验证有效，适合需要自主导航的机器人
本文提出一种基于学习的室内移动机器人导航框架。该方法将监督学习训练的神经网络全局规划器（基于成本感知的A*专家轨迹）与提出的基于学习的DWA局部规划器相结合，后者在动态窗口法的动作网格上进行离散候选选择。局部规划策略首先通过行为克隆训练，再在可行性感知掩码下使用近端策略优化（PPO）进行精炼。该框架在仿真与真实室内环境中实现并评估。实验结果表明，所提方法能生成可行的全局路径和可靠的局部运动指令，实现障碍物存在下的安全目标导向导航。结果验证了将学习型全局规划与强化学习优化的局部控制相结合在室内机器人导航中的有效性。源代码将发布于 https://ntdathp.github.io/rl_robot_web/。
原文摘要 · Abstract (English)
This paper presents a learning-based navigation framework for indoor mobile robots. The proposed method combines a supervised neural global planner, trained from cost-aware A* expert trajectories, with the proposed Learning-Based DWA local planner, which is formulated as discrete candidate selection over the Dynamic Window Approach (DWA) action lattice. For local planning, the policy is first trained by behavior cloning and then refined by Proximal Policy Optimization (PPO) under feasibility-aware masking. The framework is implemented and evaluated in both simulated and real-world indoor environments. Experimental results show that the proposed method generates feasible global routes and reliable local motion commands for safe goal-directed navigation in the presence of obstacles. These results demonstrate the effectiveness of integrating learning-based global planning with reinforcement-learning-refined local control for indoor mobile robot navigation. The source code will be released at https://ntdathp.github.io/rl_robot_web/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。