Crowd-Aware Robot Navigation with Switching between Learning-Based and Rule-Based Methods Using Normalizing Flows

Mobile robot navigation in crowded environments with pedestrians is a crucial challenge in realizing service robots that can assist people in their daily lives. Navigation methods for mobile robots in environments employing deep reinforcement learning have been extensively studied. However, addressing such unexpected situations is a significant challenge. This study presents an approach that discerns whether a situation has been supposed to utilize a normalizing flow and dynamically switches between learning- and rule-based methods. Specifically, the proposed method achieves a higher success rate than employing only a learning-based approach and reaches the destination faster than employing only a rule-based approach in unexpected situations. Experiments are conducted to validate the performance enhancement achieved with the proposed switching method in both simulated and real-world settings.