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.
In order to make a humanoid robot to resemble a human, individual behaviours are created based on simple primitive rules. These behaviours will be synchronized together to perform a more useful tasks. In this work, various robot behaviours that are required to play soccer using simple primitive rules are developed and these individual behaviours will be synchronized together so that the humanoid robot is able to play soccer autonomously. The behaviours created are separated into two categories namely object detection behaviours and motion behaviours. In object detection behaviours consists of Red Ball Detection which uses primitive rules of colour blob segmentation and depth estimation through trigonometric properties whereas Goal Post Detection uses the primitive rule of grid-based segmentation pattern learning and recognition. In motion behaviours consists of Scoring, Kip Up and Diving which uses the primitive rule of support polygon and centre of mass. Once these behaviours are designed and created, it is synchronized into two different roles namely player and keeper.
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