Gilbreth: A Conveyor-Belt Based Pick-and-Sort Industrial Robotics Application

Yizhe Zhang, Lianjun Li, M. Ripperger, J. Nicho, M. Veeraraghavan, A. Fumagalli
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引用次数: 23

Abstract

This paper describes an industrial robotics application, named Gilbreth, for picking up objects of different types from a moving conveyor belt and sorting the objects into bins according to type. The environment, which consists of a moving conveyor belt, a break beam sensor, a 3D camera Kinect sensor, a UR10 industrial robot arm with a vacuum gripper, and different object types such as gears, pulleys, piston rods, was inspired by the NIST ARIAC competition. A first version of the Gilbreth application was implemented leveraging many ROS and ROS-I packages. Gazebo was used to simulate the environment, and six external ROS nodes were implemented to execute the required functions. Experimental measurements of CPU usage and processing times of ROS nodes were obtained. Object recognition required the highest processing times that were on par with the time required for the robot arm to execute its movement between four poses: pick approach, pick, pick retreat and place. A need for enhancing the performance of object recognition and Gazebo simulation was identified.
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基于传送带的拾取分拣工业机器人应用
本文描述了一个名为Gilbreth的工业机器人应用程序,用于从移动的传送带上拾取不同类型的物体,并根据类型将物体分类到垃圾箱中。该环境由移动传送带、断梁传感器、3D摄像头Kinect传感器、带真空抓手的UR10工业机器人手臂以及不同类型的物体(如齿轮、滑轮、活塞杆)组成,灵感来自NIST ARIAC竞赛。Gilbreth应用程序的第一个版本是利用许多ROS和ROS- i包实现的。使用Gazebo来模拟环境,并实现了六个外部ROS节点来执行所需的功能。实验测量了ROS节点的CPU使用率和处理时间。物体识别需要最高的处理时间,与机器人手臂在四种姿势之间执行运动所需的时间相当:拾取接近,拾取,拾取后退和放置。指出了目标识别和Gazebo仿真性能有待提高的问题。
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