Environmental complexity control for vision-based learning mobile robot

Abstract

Discusses how a robot can develop its state vector according to the complexity of the interactions with its environment. A method for controlling the complexity is proposed for a vision-based mobile robot whose task is to shoot a ball into a goal avoiding collisions with a goalkeeper. First, we provide the most difficult situation (the maximum speed of the goalkeeper with chasing-a-ball behavior), and the robot estimates the full set of state vectors with the order of the major vector components by a method of system identification. The environmental complexity is defined in terms of the speed of the goalkeeper while the complexity of the state vector is the number of the dimensions of the state vector. According to the increase of the speed of the goalkeeper, the dimension of the state vector is increased by taking a trade-off between the size of the state space (the dimension) and the learning time. Simulations are shown, and other issues for the complexity control are discussed.