| Summary: | Many features have to be solved by humanoid robot during soccer game to get evidences from the environment such as detect ball, goal, lines and other robotmates. Having these data, the robot has to self-localize and proceed for next action reactively and ensure sense–think–act process efficiently. Sense–think–act processes are still a challenge task for humanoid robots. Hence, a modular framework is proposed for soccer ball game in which the architecture is mainly composed of object detection, field detection and motion synchronization behaviours. Object detection is modularized into ball detection, segmentation and depth estimation to facilitate the control actions. Similarly, field detection is modularized into goalpost and boundaries detection. Motion synchronization is modularized into primitives such as scoring, kip up and diving which uses the proposed support polygon and centre of moment methods. The behaviour synchronization and execution takes place in multilayers which include player and keeper mode as expert layer, modular behaviours as reactive layers and servo and motor command are executed in skill layer. The behaviour analysis and performance are targeted on the trigonometric depth estimation, grid-based segmentation pattern learning and recognition as well as support polygon and Centre Of Mass (COM). Experimental results are demonstrated and discussed. The proposed modular framework in this work has been tested using the NAO robot.
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