Robot soccer game is one of the significant and interesting areas among most of the autonomous robotic researches. Following the humanoid soccer robot basic movement and strategy actions, the robot is operated in a dynamic and unpredictable contest environment and must recognize the position of itselfin the fi eld all the time. Therefore, the localization system of the soccer robot becomes the key technology to improve the performance. This work proposes efficient approachesfor humanoid robot and uses one landmark to accomplish the self-localization. This localization mechanism integrates the inf ormation from the pan/tilt motors and a single camera on the robot head together with the artificial neural network technique to adaptively adjust the humanoid robot position. The neural network approach can improve the precision of the localization. The experimental results indicate that the average accuracy ratio is 88.5% underframe rate of 15 frames per second (fps), and the average error for the distance between the actual position and the measured position ofthe object is 6.68cm.
The research of autonomous robots is one of the most important issues in recent years. In the numerous robot researches, the humanoid robot soccer competition is very popular. The robot soccer players rely on their vision systems very heavily when they are in the unpredictable and dynamic environments. This paper proposes a simple and fast real-time object recognition system for the RoboCup soccer humanoid league rules ofthe 2009 competition. This vision system can help the robot to collect various environment information as the terminal data to finish the functions of robot localization, robot tactic, barrier avoiding,..., etc. It can decrease the computing efforts by using our proposed approach, Adaptive Resolution Method (ARM), to recognize the critical objects in the contest fi eld by object features which can be obtained easily. The experimental results indicate that the proposed approach can increase the real time and accurate recognition efficiency.
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