This paper presents a simulator for the behaviors of swarm robots based on a Dynamic Bayesian Network (DBN). Our task is to design each robot's controller which enables the robot to patrol as many regions as possible without collisions. As the first step, we use two swarm robots, each of which has two motors each of which is connected to a wheel and three distance-measurement sensors. To design the controllers of these robots, we must determine several parameters such as the motor speed and thresholds of the three sensors. The simulator is used to reduce the number of real experiments in deciding values of such parameters. We fist performed measurement experiments for our real robots in order to get probabilistic data of the DBN. The simulator based on the DBN revealed appropriate values of a threshold parameter and interesting phase transitions of their behaviors in terms of the values.