Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots

Classical map-based navigation methods are commonly used for robot navigation, but they often struggle in crowded environments due to the Frozen Robot Problem (FRP). Deep reinforcement learning-based methods address the FRP problem, however, suffer from the issues of generalization and scalability. To overcome these challenges, we propose a method that uses Collision Probability (CP) to help the robot navigate safely through crowds. The inclusion of CP in the observation space gives the robot a sense of the level of danger of the moving crowd. The robot will navigate through the crowd when it appears safe but will take a detour when the crowd is moving aggressively. By focusing on the most dangerous obstacle, the robot will not be confused when the crowd density is high, ensuring scalability of the model. Our approach was developed using deep reinforcement learning (DRL) and trained using the Gazebo simulator in a non cooperative crowd environment with obstacles moving at randomized speeds and directions. We then evaluated our model on four different crowd-behavior scenarios with varying densities of crowds. The results shown that our method achieved a 100% success rate in all test settings. We compared our approach with a current state-of-the-art DRLbased approach, and our approach has performed significantly better. Importantly, our method is highly generalizable and requires no fine-tuning after being trained once. We further demonstrated the crowd navigation capability of our model in real-world tests.