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    机器人抓取汇总|涉及目标检测、分割、姿态识别、抓取点检测、路径规划

    最近读了一些关于机器人抓取相关内容的文章,觉得甚是不错,针对一些方法和知识点,做下总结。本文综述了基于视觉的机器人抓取技术,总结了机器人抓取过程中的四个关键任务:目标定位、姿态估计、抓取检测和运动规划。具体来说,目标定位包括目标检测和分割方法,姿态估计包括基于RGB和RGBD的方法,抓取检测包括传统方法和基于深度学习的方法,运动规划包括分析方法、模拟学习方法和强化学习方法。此外,许多方法共同完成了一些任务,如目标检测结合6D位姿估计、无位姿估计的抓取检测、端到端抓取检测、端到端运动规划等。本文对这些方法进行了详细的综述,此外,还对相关数据集进行了总结,并对每项任务的最新方法进行了比较。提出了机器人抓取面临的挑战,并指出了今后解决这些挑战的方向。

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    遮挡重叠场景下|基于卷积神经网络与RoI方式的机器人抓取检测

    抓取物体堆叠和重叠场景中的特定目标是实现机器人抓取的必要和具有挑战性的任务。在本文中,我们提出了一种基于感兴趣区域(RoI)的机器人抓取检测算法,以同时检测目标及其在物体重叠场景中的抓取。我们提出的算法使用感兴趣区域(RoIs)来检测目标的分类和位置回归。为了训练网络,我们提供了比Cornell Grasp Dataset更大的多对象抓取数据集,该数据集基于Visual Manipulation Relationship Dataset。实验结果表明,我们的算法在1FPPI时达到24.9%的失误率,在抓取我们的数据集时达到68.2%的mAP。机器人实验表明,我们提出的算法可以帮助机器人以84%的成功率掌握多物体场景中的特定目标。

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    FogROS2 使用 ROS 2 的云和雾机器人的自适应和可扩展平台

    FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2 Abstract— Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run modern robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power on demand, but tapping into that power from a robot is non-trivial. In this paper, we present FogROS2, an easy-to-use, open-source platform to facilitate cloud and fog robotics that is compatible with the emerging Robot Operating System 2 (ROS 2) standard. FogROS 2 provisions a cloud computer, deploys and launches ROS 2 nodes to the cloud computer, sets up secure networking between the robot and cloud, and starts the application running. FogROS 2 is completely redesigned and distinct from its predecessor to support ROS 2 applications, transparent video compression and communication, improved performance and security, support for multiple cloud-computing providers, and remote monitoring and visualization. We demonstrate in example applications that the performance gained by using cloud computers can overcome the network latency to significantly speed up robot performance. In examples, FogROS 2 reduces SLAM latency by 50%, reduces grasp planning time from 14s to 1.2s, and speeds up motion planning 28x. When compared to alternatives, FogROS 2 reduces network utilization by up to 3.8x. FogROS2, source, examples, and documentation is available at github.com/BerkeleyAutomation/FogROS2.

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