Robotic Grasping of Unknown Objects

Non

Grasping of Unknown Objects from a Table Top

International audienceThis paper describes the development of a novel vision-based grasping system for unknown objects based on range images. We realize a synthesis of the calculated grasp points with a 3D model of a hand prosthesis, which we are using as gripper. We locally find grasp point candidates based on the shape of the object and validate the globally by checking collisions between the gripper and surrounding objects and the table top. Our approach integrates a robust object segmentation and grasp point detection for every object on a table in front of a 7-DOF robot arm. The algorithm analyzes the top surface of every object and outputs the generated grasp points and the required gripper pose to grasp the desired object. Additionally we can calculate the optimal opening angle of the gripper. The first experimental results show that the presented automated grasping system is able to generate successful grasp points for a wide range of different objects

Real-Time Laser Profile Edge Detection by Voting 1) Abstract:

Today, automobile and aircraft industry uses more and more robust and light-weight parts made of carbon fibres. To achieve high structural properties the carbon fibre mats are stitched in the preforming process using a sewing robot. The goal of this work is to develop an edge tracking system that continuously follows the seams. To work with the black and reflective carbon fibres a laser-scan sensor is used, though online processing of the range data for seam-tracking remains a challenging task. The paper presents a fast and robust two-out-of-three voting image processing method to track the seam and to control the robot motion in real-time. The experimental results demonstrate the feasibility of the fully automated, sensor-guided robotic sewing process.