Environmental Complexity Control for Vision-Based Learning Mobile Robot

This paper 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 of which task is to shoot a ball into a goal avoiding collisions with a goal keeper. First, we provide the most di cult situation (the maximum speed of the goal keeper 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 identi cation. The environmental complexity is de ned in terms of the speed of the goal keeper 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 goal keeper, the dimension of the state vector is increased by taking a trade-o 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.