Low-cost Solution for Vision-based Robotic Grasping

Abstract

Robotic grasping is a fundamental task for many robots to interact with the outside world, and it is still challenging. There are at least three tasks for robot grasping: object localization, grasp pose estimation, and motion planning. This paper presents a low-cost machine vision solution for robotic grasping based on template matching, including comparisons between different approaches, including state-of-the-art YOLOv4 object detection and edge-based geometric shape detection. The robotic grasping solution presented in this paper shows a high pick-and-place success rate. An improvement for template matching is implemented in this paper as well. This paper also provides detailed analysis, algorithms, and experiments.