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仿人足球机器人快速多目标识别方法

Fast Multi-Target Recognition Method for Humanoid Robot Playing Soccer

  • 摘要: 针对全自主仿人足球机器人在自身动态特性显著和比赛场地复杂环境条件下快速准确识别多种目标物的要求,采用融合形态学分析的类Haar特征构造热图的方法对机器人视域内的疑似目标物进行粗略选取,形成“感兴趣区域”.再构造轻量级Tiny-DNN卷积神经网络对“感兴趣区域”进行快速分类,准确判断这些区域是否包含目标物.机器人运行过程中自动生成的大量“感兴趣区域”作为卷积神经网络的离线训练过程正负样本集,避免了在海量图片上人为标注目标物样本的大量工作与耗时及不确定性偏差.该方法应用于搭载CPU处理器的SYCU-Legendary仿人机器人,使其能够在0.03 s内对于目标物足球、球门柱和罚球点的识别率分别达到95.8%,96.2%和96.0%.目标识别过程受比赛场地异物和光线变化影响较小,夺得了2018年和2019年足球机器人世界比赛(RoboCup)中国赛区冠军.实际应用结果表明该方法具有较高的推广应用价值.

     

    Abstract: Fully autonomous humanoid robot soccer competition requires the dynamic-featured robot recognizing multi-target both accurately and rapidly under complex field environment.A fast multi-target recognition method for humanoid soccer robot is proposed.Haar-like features integrated with morphological analysis is employed to construct heat maps,on which the candidate patches supposed containing object in the robot’s field of view are roughly selected to form a set of“region of interest(ROI)”.This ROI set is quickly classified into target or non-target regions through a lightweight Tiny-DNN convolution neural network.Meanwhile,each ROI generated automatically during the robot operation can be directly collected and stacked into positive or negative sample sets for the off-line training of the convolution neural network,thus avoiding tremendous labor work and uncertainty deviation of manually tagger of target sample on quantities of pictures.The proposed method is applied to CPU supported SYCU-Legendary humanoid robot,enabling it to recognize 95.8%,96.2%and 96.0%of football,goalposts and penalty marks within 0.03 seconds.Besides,foreign matters and light changes cast little influence on targets recognition so that SYCU-Legendary team won the championship in 2018 and 2019 RoboCup China Open,which indicates the method in this paper is worthy to be widely extended to other application domains.

     

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