Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.