PDF(Pubmed)
Abstract：
Mobile robots are playing an increasingly significant role in social life and industrial production, such as searching and rescuing robots, autonomous exploration of sweeping robots, and so on. Improving the accuracy of autonomous navigation of mobile robots is a hot issue to be solved. However, traditional navigation methods are unable to realize crash-free navigation in an environment with dynamic obstacles, more and more scholars are gradually using autonomous navigation based on deep reinforcement learning (DRL) to replace overly conservative traditional methods. But on the other hand, DRL\'s training time is too long, and the lack of long-term memory easily leads the robot to a dead end, which makes its application in the actual scene more difficult. To shorten training time and prevent mobile robots from getting stuck and spinning around, we design a new robot autonomous navigation framework which combines the traditional global planning and the local planning based on DRL. Therefore, the entire navigation process can be transformed into first using traditional navigation algorithms to find the global path, then searching for several high-value landmarks on the global path, and then using the DRL algorithm to move the mobile robot toward the designated landmarks to complete the final navigation, which makes the robot training difficulty greatly reduced. Furthermore, in order to improve the lack of long-term memory in deep reinforcement learning, we design a feature extraction network containing memory modules to preserve the long-term dependence of input features. Through comparing our methods with traditional navigation methods and reinforcement learning based on end-to-end depth navigation methods, it shows that while the number of dynamic obstacles is large and obstacles are rapidly moving, our proposed method is, on average, 20% better than the second ranked method in navigation efficiency (navigation time and navigation paths\' length), 34% better than the second ranked method in safety (collision times), 26.6% higher than the second ranked method in success rate, and shows strong robustness.
摘要：
移动机器人在社会生活和工业生产中发挥着越来越重要的作用,比如搜寻和营救机器人，自主探索扫地机器人，等等。提高移动机器人自主导航的精度是亟待解决的热点问题。然而,传统的导航方法无法在动态障碍物环境中实现无碰撞导航，越来越多的学者正在逐步使用基于深度强化学习(DRL)的自主导航方法来代替过于保守的传统方法。但另一方面,DRL的训练时间太长,缺乏长期记忆很容易导致机器人陷入死胡同，这使得其在实际场景中的应用更加困难。为缩短训练时间,防止移动机器人被卡住,转来转去,结合传统的全局规划和基于DRL的局部规划,设计了一种新的机器人自主导航框架。因此,整个导航过程可以转化为首先使用传统的导航算法来找到全局路径，然后在全球路径上寻找几个高价值的地标，然后使用DRL算法将移动机器人向指定的地标移动以完成最终导航，这使得机器人训练难度大大降低。此外，为了改善深度强化学习中缺乏长期记忆的问题，我们设计了一个包含内存模块的特征提取网络，以保持输入特征的长期依赖性。通过将我们的方法与传统导航方法以及基于端到端深度导航方法的强化学习进行比较，它表明，尽管动态障碍物的数量很大，并且障碍物正在迅速移动，我们提出的方法是，平均而言,在导航效率（导航时间和导航路径长度）方面比排名第二的方法好20％，在安全性（碰撞次数）方面比排名第二的方法好34％，成功率比排名第二的方法高26.6%，并表现出较强的鲁棒性。
