论文标题
机器人导航，并通过增强学习路径生成和微调运动控制
Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control
论文作者
论文摘要
在本文中，我们提出了一种新型的增强学习（RL）的路径生成（RL-PG）方法，用于移动机器人导航，而无需事先探索未知环境。多个预测路径点是通过使用RL方法进行的，可以通过Robot进行跟踪的深度Markov模型动态生成。为了确保在跟踪预测点时的安全性，机器人的运动通过运动微调模块进行了微调。这种方法是使用带有RL算法的Deep Markov模型进行计划的方法，重点是相邻路径点之间的关系。我们分析了我们提出的方法更有效的好处，并且比基于RL的方法DWA-RL和传统导航方法APF具有更高的成功率。我们将模型部署在模拟和物理平台上，并演示我们的模型有效，安全地执行机器人导航。
In this paper, we propose a novel reinforcement learning (RL) based path generation (RL-PG) approach for mobile robot navigation without a prior exploration of an unknown environment. Multiple predictive path points are dynamically generated by a deep Markov model optimized using RL approach for robot to track. To ensure the safety when tracking the predictive points, the robot's motion is fine-tuned by a motion fine-tuning module. Such an approach, using the deep Markov model with RL algorithm for planning, focuses on the relationship between adjacent path points. We analyze the benefits that our proposed approach are more effective and are with higher success rate than RL-Based approach DWA-RL and a traditional navigation approach APF. We deploy our model on both simulation and physical platforms and demonstrate our model performs robot navigation effectively and safely.
