Playing Creative Soccer: Randomized Behavioral Kinodynamic Planning of Robot Tactics

Modern robot soccer control architectures tend to separate higher level tactics and lower level navigation control. This can lead to tactics which do not fully utilize the robot’s dynamic actuation abilities. It can furthermore create the problem of the navigational code breaking the constraints of the higher level tactical goals when avoiding obstacles. We aim to improve such control architectures by modeling tactics as sampling-based behaviors which exist inside of a probabilistic kinodynamic planner, thus treating tactics and navigation as a unified dynamics problem. We present a behavioral version of Kinodynamic RapidlyExploring Random Trees and show that this algorithm can be used to automatically improvise new ball-manipulation strategies in a simulated robot soccer domain. We furthermore show how opponent-models can be seamlessly integrated into the planner, thus allowing the robot to anticipate and outperform the opponent’s motions in physics-space.