Robot navigation in crowds via deep reinforcement learning with modeling of obstacle uni-action
Robot navigation in crowds via deep reinforcement learning with modeling of obstacle uni-action
复制标题
通过深度强化学习和障碍单动作建模实现机器人在人群中的导航
DOI：
10.1080/01691864.2022.2142068
复制
发表时间：
2022
影响因子：
2
通讯作者：
Asama Hajime
中科院分区：
文献类型：
--
作者：
Lu Xiaojun;Woo Hanwool;Faragasso Angela;Yamashita Atsushi;Asama Hajime
在公共环境中运行的移动机器人需要能够以符合社会规范且安全的方式在人类和障碍物之间导航。之前的工作通过使用深度强化学习（DRL）技术来训练有效的机器人导航策略，展示了深度强化学习（DRL）技术的力量。然而，大多数基于DRL的机器人导航方法只考虑动态行人，没有考虑静态障碍物。以不同方式接近行人和障碍物的能力将提高机器人的导航效率。在这项工作中，我们提出了一种新颖的网络，即障碍机器人单向（ORU）网络，来编码障碍物对机器人的单向直接影响。障碍物对机器人的间接影响，以障碍物与人的单向动作（OHU）以及人与人的交互（HHI）为代表，被连接到人与机器人的交互（HRI）网络中，以获得人群对机器人的影响的特征。我们还在我们的模型中实现了变量达到目标奖励和接近目标奖励，这可以提高该方法在导航时间方面的性能。模拟和真实数据集的测试实验结果表明，所提出的方法优于最先进的方法。
Mobile robots operating in public environments require the ability to navigate among humans and obstacles in a socially compliant and safe manner. Previous work has shown the power of deep reinforcement learning (DRL) techniques by employing them to train efficient policies for robot navigation. However, most DRL-based robot navigation methods only consider dynamic pedestrians and do not take static obstacles into account. The ability to approach pedestrians and obstacles differently will improve a robot's navigation efficiency. In this work, we propose a novel network, the obstacle-robot uni-action (ORU) network, to encode the one-way direct effects of obstacles on the robot. Obstacles' indirect effects on the robot, represented by the obstacle-human uni-action (OHU), together with human–human interaction (HHI), are concatenated to a human–robot interaction (HRI) network to obtain the features of a crowd's effects on the robot. We also implement a variable reaching goal reward and an approaching goal reward in our model, which can enhance the method's performance in terms of navigation time. Results of test experiments in both simulation and real datasets demonstrate that the proposed method outperforms state-of-the-art methods.
影响因子：
3.5
作者：
Ferrer, Gonzalo;Garrell Zulueta, Anais;Sanfeliu, Alberto
通讯作者：
Sanfeliu, Alberto