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Current state-of-the-art crowd navigation approaches are mainly deepreinforcement learning (DRL)-based. However, DRL-based methods suffer from theissues of generalization and scalability. To overcome these challenges, wepropose a method that includes a Collision Probability (CP) in the observationspace to give the robot a sense of the level of danger of the moving crowd tohelp the robot navigate safely through crowds with unseen behaviors. We studiedthe effects of changing the number of moving obstacles to pay attention duringnavigation. During training, we generated local waypoints to increase thereward density and improve the learning efficiency of the system. Our approachwas developed using deep reinforcement learning (DRL) and trained using theGazebo simulator in a non-cooperative crowd environment with obstacles movingat randomized speeds and directions. We then evaluated our model on fourdifferent crowd-behavior scenarios. The results show that our method achieved a100% success rate in all test settings. We compared our approach with a currentstate-of-the-art DRL-based approach, and our approach has performedsignificantly better, especially in terms of social safety. Importantly, ourmethod can navigate in different crowd behaviors and requires no fine-tuningafter being trained once. We further demonstrated the crowd navigationcapability of our model in real-world tests.

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