Dynamic Adaptive Dynamic Window Approach

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 the Proximal Policy Optimization (PPO) 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 3D spaces and show that it outperforms both DWA and purely Deep Learning approaches.