Abstract:

Taking advantage of its high?speed and high?precision characteristics, cooperative robots can improve production efficiency by imitating human creative and complex actions. At present, the simulation of human action mainly comes from the long?term debugging of the deployment personnel, which is lack of general solutions and can′t be deployed quickly. Based on this, an anchor?free RepVGG network?based Siamese network collaborative robot target tracking algorithm was proposed. The algorithm consists of a siamese network module, a classification regression module and a robot execution module. The siamese network module used the improved RepVGG network instead of ResNet as the backbone network to extract image features, which can improve the running speed of the whole network without losing accuracy，reduce the hardware requirements and is more friendly to special deep learning chips; the classification and regression module introduced the centrality branch to improve the prediction accuracy of the center point of the tracking frame; the robot execution module uses scale penalty and aspect ratio penalty to smooth the tracking boxes and ensure the smooth operation of the collaborative robots. Experimental results show that the average rate is 14 FPS higher than that before replacing the backbone network, which realizes real?time object tracking.

Key words: Object tracking, Siamese network, RepVGG network, Deep learning, Cooperative robot

摘要：

利用自身高速高精度的特点，协作机器人通过模仿人的创造性复杂动作来提高生产效率。当前协作机器人对人动作的模仿主要来自部署人员的长期调试，缺少通用的解决方案，无法快速部署。基于此，提出了一种无锚的基于RepVGG网络的孪生网络协作机器人目标跟踪算法。该算法由孪生网络模块、分类回归模块和机器人执行模块组成。孪生网络模块使用改进的RepVGG网络代替主流的ResNet作为骨干网络用于图片特征的提取，在不损失精度的前提下提高整个网络的运行速度，降低算法对硬件的要求，对专用深度学习芯片更加友好；分类回归模块通过引入中心度分支来提高跟踪框的中心点预测精度；机器人执行模块采用尺度惩罚和宽高比惩罚以平滑跟踪框，保证协作机器人的动作流畅。实验结果表明,平均速率相比替代ResNet骨干网络前提高了14 FPS，实现了实时跟踪的效果。

关键词: 目标跟踪, 孪生网络, RepVGG网络, 深度学习, 协作机器人