Robust local navigation is a critical capability for any mobile robot operating in a real-world unstructured environment, especially when there are humans or other moving obstacles in the workspace. One of the most commonly used methods for local navigation is the dynamic window approach (DWA), which does not address the problem of dynamic obstacles and depends heavily on the settings of the parameters in its cost function. Thus, it is a static approach that does not adapt to the characteristics of the environment, which can change significantly. On the other hand, data-driven deep learning approaches attempt to adapt to the characteristics of the environment by predicting the appropriate robot motion based on the current observation. However, they cannot guarantee collision-free trajectories for unseen inputs. In this work, we combine the best of both worlds. We propose a neural network to predict the weights of the DWA, which is then used for safe local navigation. To address the problem of dynamic obstacles, the proposed method considers a short sequence of observations to allow the network to model the motion of the obstacles and adjust the DWA weights accordingly. The network is trained using proximal policy optimization in a reinforcement learning setting in a simulated dynamic environment. We perform a comprehensive evaluation of the proposed approach in realistic scenarios using range scans of real 3-D spaces and show that it outperforms both DWA and purely deep learning approaches.

中文翻译：

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动态自适应动态窗口方法


强大的本地导航对于在现实世界的非结构化环境中运行的任何移动机器人来说都是一项关键功能，特别是当工作空间中有人类或其他移动障碍物时。本地导航最常用的方法之一是动态窗口方法（DWA），它没有解决动态障碍物的问题，并且严重依赖于其成本函数中参数的设置。因此，它是一种静态方法，无法适应可能发生显着变化的环境特征。另一方面，数据驱动的深度学习方法试图通过基于当前观察预测适当的机器人运动来适应环境的特征。然而，它们不能保证未见过的输入的无碰撞轨迹。在这项工作中，我们结合了两全其美。我们提出了一个神经网络来预测 DWA 的权重，然后将其用于安全的本地导航。为了解决动态障碍物的问题，所提出的方法考虑了短序列的观察，以允许网络对障碍物的运动进行建模并相应地调整 DWA 权重。该网络在模拟动态环境的强化学习设置中使用近端策略优化进行训练。我们使用真实 3D 空间的范围扫描在现实场景中对所提出的方法进行了全面评估，并表明它优于 DWA 和纯深度学习方法。