Behaviour coordinations and motion synchronizations for humanoid robot

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. S...

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Main Authors: S Parasuraman, Phua Seong Hock, MKA Ahamed Khan, D Kingsly Jeba Singh, Chin Yun Han
Format: Article
Language:English
Published: SAGE Publishing 2017-10-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/1729881417728453
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author S Parasuraman
Phua Seong Hock
MKA Ahamed Khan
D Kingsly Jeba Singh
Chin Yun Han
author_facet S Parasuraman
Phua Seong Hock
MKA Ahamed Khan
D Kingsly Jeba Singh
Chin Yun Han
author_sort S Parasuraman
collection DOAJ
description 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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spelling doaj.art-3561c70d318748359cf519dafb0cb8f02022-12-21T18:44:17ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142017-10-011410.1177/1729881417728453Behaviour coordinations and motion synchronizations for humanoid robotS Parasuraman0Phua Seong Hock1MKA Ahamed Khan2D Kingsly Jeba Singh3Chin Yun Han4 School of Engineering, Monash University Malaysia, Petaling Jaya, Selangor, Malaysia Faculty of Engineering, University Selangor, Malaysia Faculty of Engineering, University Selangor, Malaysia Mechanical Engineering, SRM University, Chennai, India School of Engineering, Monash University Malaysia, Petaling Jaya, Selangor, MalaysiaMany 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.https://doi.org/10.1177/1729881417728453
spellingShingle S Parasuraman
Phua Seong Hock
MKA Ahamed Khan
D Kingsly Jeba Singh
Chin Yun Han
Behaviour coordinations and motion synchronizations for humanoid robot
International Journal of Advanced Robotic Systems
title Behaviour coordinations and motion synchronizations for humanoid robot
title_full Behaviour coordinations and motion synchronizations for humanoid robot
title_fullStr Behaviour coordinations and motion synchronizations for humanoid robot
title_full_unstemmed Behaviour coordinations and motion synchronizations for humanoid robot
title_short Behaviour coordinations and motion synchronizations for humanoid robot
title_sort behaviour coordinations and motion synchronizations for humanoid robot
url https://doi.org/10.1177/1729881417728453
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AT mkaahamedkhan behaviourcoordinationsandmotionsynchronizationsforhumanoidrobot
AT dkingslyjebasingh behaviourcoordinationsandmotionsynchronizationsforhumanoidrobot
AT chinyunhan behaviourcoordinationsandmotionsynchronizationsforhumanoidrobot