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Deep reinforcement learning for map-less goal-driven robot navigation
International Journal of Advanced Robotic Systems ( IF 2.3 ) Pub Date : 2021-02-24 , DOI: 10.1177/1729881421992621
Matej Dobrevski 1 , Danijel Skočaj 1
Affiliation  

Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot.



中文翻译:

用于无需地图的目标驱动机器人导航的深度强化学习

在现实环境中运行的移动机器人需要能够安全地导航其周围环境。避障和路径规划是实现此类系统自治的关键功能。但是,对于新的或动态的环境,依赖于环境的显式地图的导航方法可能不切实际甚至无法使用。我们提出了一种新的本地导航方法,无需依赖明确的环境地图即可将机器人导向全球目标。所提出的导航模型在基于Advantage Actor-Critic方法的深度强化学习框架中进行了训练,并且能够将机器人的观察结果直接转换为运动命令。我们在仿真中的几种导航方案中评估并比较了所提出的导航方法与基于标准地图的方法,并证明了在标准方法失败的情况下,我们的方法也能够在没有地图或地图损坏的情况下导航机器人。我们还表明,我们的方法可以直接转移到真实的机器人上。

更新日期:2021-02-24
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