An Interception Tree Approach In Wheeled Soccer Robots

The development of autonomous soccer robots has engaged numerous researchers around the world and poses a wide variety of challenges in not only encompassing mechanical components but also control strategy, where predicting the adversarial action plays a pivotal role. In this paper, we present our interception prediction approach based on the tree data structure called “interception tree”. Each pair of connected nodes represents an adversarial relation, where we predict the child optimal interception on the parent's trajectory. The tree gives out the kick plan's risk factor and is used for further strategy guidance in the shoot, pass and defense tactics. We demonstrate the effectiveness of our algorithm in terms of kicking and team performance through our 1200 simulated matches result over 6 set-ups which combine the different formations and defense strategies.