基于强化学习的移动机器人局部路径规划
Reinforcement learning based local path planning for mobile robot
AI总结：
本研究评估基于深度Q-Learning（DQN）和深度DQN的强化学习方法，用于移动机器人在线局部路径规划，以解决离线重规划与在线数据需求问题，实现避障导航。
摘要：
移动机器人前往特定目标位置时可采用不同的方法。这些方法在在线和离线场景中以不同方式工作。在离线场景中，环境地图被一次性创建，并在此地图上进行固定的路径规划以到达目标。A*和RRT（快速探索随机树）等路径规划算法是离线方法的示例。这里最明显的情况是，对于已加载地图的条件变化，需要重新规划路径。另一方面，在在线场景中，机器人不使用地图，而是利用来自传感器的感知数据动态移动到给定目标。SFM（社会力模型）等方法用于在线系统。然而，这些方法存在需要大量动态传感数据的问题。因此，可以说离线系统中重新规划和地图绘制的需求以及在线系统中各种系统设计要求是自主移动机器人研究关注的焦点。最近，基于深度神经网络的Q-Learning方法被用作解决移动机器人导航中上述问题的新兴解决方案。在本研究中，评估了具有深度Q-Learning（DQN）和深度DQN架构的机器学习算法，以解决上述问题，实现自主移动机器人避开障碍物的路径规划。
英文摘要：
Different methods are used for a mobile robot to go to a specific target location. These methods work in different ways for online and offline scenarios. In the offline scenario, an environment map is created once, and fixed path planning is made on this map to reach the target. Path planning algorithms such as A* and RRT (Rapidly-Exploring Random Tree) are the examples of offline methods. The most obvious situation here is the need to re-plan the path for changing conditions of the loaded map. On the other hand, in the online scenario, the robot moves dynamically to a given target without using a map by using the perceived data coming from the sensors. Approaches such as SFM (Social Force Model) are used in online systems. However, these methods suffer from the requirement of a lot of dynamic sensing data. Thus, it can be said that the need for re-planning and mapping in offline systems and various system design requirements in online systems are the subjects that focus on autonomous mobile robot research. Recently, deep neural network powered Q-Learning methods are used as an emerging solution to the aforementioned problems in mobile robot navigation. In this study, machine learning algorithms with deep Q-Learning (DQN) and Deep DQN architectures, are evaluated for the solution of the problems presented above to realize path planning of an autonomous mobile robot to avoid obstacles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
