论文标题
DGBENCH：一种开源，可再现的基准，用于动态抓握
DGBench: An Open-Source, Reproducible Benchmark for Dynamic Grasping
论文作者
论文摘要
本文介绍了DGBENCH，这是一种完全可重现的开源测试系统，可在机器人和对象之间具有不可预测的相对运动的环境中对动态抓握进行基准测试。我们使用拟议的基准比较几种视觉感知布置。由于传感器的最小范围，遮挡和有限的视野，用于静态抓握的传统感知系统无法在掌握的最后阶段提供反馈。提出了一个多摄像机的眼睛感知系统，该系统比常用的相机配置具有优势。我们用基于图像的视觉宣传控制器进行定量评估真实机器人的性能，并在动态掌握任务上显示出明显提高的成功率。
This paper introduces DGBench, a fully reproducible open-source testing system to enable benchmarking of dynamic grasping in environments with unpredictable relative motion between robot and object. We use the proposed benchmark to compare several visual perception arrangements. Traditional perception systems developed for static grasping are unable to provide feedback during the final phase of a grasp due to sensor minimum range, occlusion, and a limited field of view. A multi-camera eye-in-hand perception system is presented that has advantages over commonly used camera configurations. We quantitatively evaluate the performance on a real robot with an image-based visual servoing grasp controller and show a significantly improved success rate on a dynamic grasping task.
