Improving the Ability of Robots to Navigate Through Crowded Environments Safely using Deep Reinforcement Learning

Autonomous robot navigation in unpredictable and crowded environments requires a guarantee of safety and a stronger ability to pass through a narrow passage. However, it's challenging to plan safe, dynamically-feasible trajectories in real-time. Previous approaches, such as Reachability-based Trajectory Design (RTD), focus on safety guarantee, but the lack of online strategy always makes the robot fail to pass through a narrow passage. This paper proposes to learn a policy that guides the robot to make successful plans using deep Reinforcement Learning (RL). We train a deep network based on the RTD method to create cost functions in real-time. The created cost function is expected to help the online planner optimize the robot's feasible trajectory, satisfying its kino-dynamics model and collision avoidance constraints. In crowded simulated environments, our approach substantially improves the planning success rate compared to RTD and some other methods.