Object Detection and Grabbing Based on Machine Vision for Service Robot

Abstract

To deal with the challenges of object detection and object grabbing tasks for service robots, we propose an object detection and segmentation algorithm based on the deep convolutional neural network (CNN) and depth-first algorithm to achieve object grabbing by using an RGB-D camera and the six-degree-of-freedom robotic manipulator. Firstly, the improved particle swarm optimization (PSO) algorithm is proposed to calibrate and optimize the hand-eye system of the experimental platform, which is a mobile robot equipped with UR5 mechanical arm and Kinect V2 sensor. Then, we collect the environmental information by the camera, where the depth images restored by the joint bilateral filtering algorithm and the original color images are calibrated. Finally, a depth learning method is used to detect the objects, and the depth information is used to achieve objects segmentation. We complete object grabbing based on the estimated 3D coordinates, which has proven the practicability and effectiveness of our proposed grabbing framework.