Robot Navigation with Map-Based Deep Reinforcement Learning
This work addresses navigation challenges for mobile robots in dynamic environments, representing an incremental improvement over existing DRL-based methods.
The paper tackles mobile robot navigation with dynamic obstacle avoidance by proposing an end-to-end deep reinforcement learning approach using convolutional neural networks trained on local occupancy maps, and it shows that the model is easy to deploy, robust to sensor noise, and outperforms other DRL-based models in many indicators.
This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict proper steering actions of a robot from its egocentric local occupancy maps, which accommodate various sensors and fusion algorithms. The trained neural network is then transferred and executed on a real-world mobile robot to guide its local path planning. The new approach is evaluated both qualitatively and quantitatively in simulation and real-world robot experiments. The results show that the map-based end-to-end navigation model is easy to be deployed to a robotic platform, robust to sensor noise and outperforms other existing DRL-based models in many indicators.