We present a computer vision solution integrated to an omnidirectional transporting robot to perform the position tracking of multiple trays moving on its planar acrylic plate surface. The trays were designed to carry lightweight materials on top of their surface so that the mechanism could be implemented as an automated transporting system for applications that require the displacement of products and/or materials in any given space. One hurdle faced by the visual system for suitable detection was the partial occlusion of the image of a tray when placing arbitrary objects on its surface. Our strategy to overcome this challenge consisted on the implementation of machine learning algorithms, such as Support Vector Machines (SVM), using datasets of images containing trays with different occlusion patterns for fast object detection through rigorous training. The results of experimental tests validate the implementation of our proposal as a reliable approach for the object tracking of multiple trays on the robotic device, even under partial occlusion. We also studied the accuracy of the position measurements performed by our visual system with respect to the position measurements taken by the OPTITRACK motion capture system and evaluated the processing time per frame required by the software implementation. GRAPHICAL ABSTRACT

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全方位搬运机器人视觉机器学习研究

我们提出了一种集成到全向运输机器人的计算机视觉解决方案，以执行在其平面亚克力板表面上移动的多个托盘的位置跟踪。托盘设计用于在其表面上承载轻质材料，因此该机构可以作为自动运输系统实施，适用于需要在任何给定空间中移动产品和/或材料的应用。视觉系统在进行适当检测时面临的一个障碍是，在托盘表面放置任意物体时，托盘图像会被部分遮挡。我们克服这一挑战的策略包括实施机器学习算法，例如支持向量机 (SVM)，通过严格的训练，使用包含具有不同遮挡模式的托盘的图像数据集进行快速目标检测。实验测试的结果验证了我们的建议作为一种可靠的方法，即使在部分遮挡的情况下，也可以在机器人设备上跟踪多个托盘的对象。我们还研究了我们的视觉系统执行的位置测量相对于 OPTITRACK 运动捕捉系统进行的位置测量的准确性，并评估了软件实现所需的每帧处理时间。图形概要 我们还研究了我们的视觉系统执行的位置测量相对于 OPTITRACK 运动捕捉系统进行的位置测量的准确性，并评估了软件实现所需的每帧处理时间。图形概要 我们还研究了我们的视觉系统执行的位置测量相对于 OPTITRACK 运动捕捉系统进行的位置测量的准确性，并评估了软件实现所需的每帧处理时间。图形概要