Fast decision making of autonomous robot under dynamic environment by sampling real-time Q-MDP value method

In this paper, the sampling real-time Q_MDP value method is proposed and applied to a goalkeeper task of a soccer robot. Soccer is a challenging task for an autonomous robot that has poor computing resources and sensors, and a good subject in the study of decision making in dynamic environments. A robot frequently decides its behavior without enough observation of the environment. In the proposed method, the risk of a score is solved beforehand toward every set of position of the goalkeeper and motion of the ball. The shortage of observation is calculated and represented by particle filters. The proposed method uses the risk function and the particle filters so as to choose appropriate behavior of the goalkeeper. The experiment with an actual robot suggests that the method can decide actions reflexively toward the motion of the ball.