Evolutionary learning of control and strategies in robot soccer

"Robot soccer provides a fertile environment for the development of artificial intelligence techniques. Robot controls require high speed lower level reactive layers as well as higher level deliberative functions. This thesis focuses on a number of aspects in the robot soccer arena. Topics covered include boundary avoidance strategies, vision detection and the application of evolutionary learning to find fuzzy controllers for the control of mobile robot. A three input, two output controller using two angles and a distance as the input and producing two wheel velocity outputs, was developed using evolutionary learning. Current wheel velocities were excluded from the input. The controller produced was a coarse control permitting only either forward or reverse facing impact with the ball. A five input controller was developed which expanded upon the three input model by including the current wheel velocities as inputs. The controller allowed both forward and reverse facing impacts with the ball. A five input hierarchical three layer model was developed to reduce the number of rules to be learnt by an evolutionary algorithm. Its performance was the same as the five input model. Fuzzy clustering of evolved paths was limited by the information available from the paths. The information was sparse in many areas and did not produce a controller that could be used to control the robots. Research was also conducted on the derivation of simple obstacle avoidance strategies for robot soccer. A new decision region method for colour detection in the UV colour map to enable better detection of the robots using an overhead vision system. Experimental observations are given." -- abstract