Adaptive Navigation Method for Mobile Robots in Various Environments using Multiple Control Policies

In recent years, to achieve safe and efficient navigation for autonomous mobile robots, several methods have been proposed to switch between multiple policies (action decision methods), including deep reinforcement learning, depending on the situation. We have also proposed methods to introduce new policy-switching criteria and to add new policies to avoid freezing conditions. Compared to existing methods, we have shown that the method improves safety metric values (e.g., collision rate) even in narrow corridors; however, there are still challenges in achieving sufficient performance because safety and efficiency metric values fluctuate depending on the environment. In this paper, we propose an adaptive navigation method that uses sensing results to classify robot deployment environments into several groups and adaptively changes the policy-switching algorithm according to the environment. Specifically, we use collision risk and congestion level for the environment classification and associate the environment classes with appropriate control parameter values (i.e., parameter tuning) to achieve adaptive navigation. Furthermore, we verify the effectiveness of the proposed method by conducting simulation experiments.