Robotic grasp detection based on image processing and random forest

Zhang, Jiahao; Li, Miao; Feng, Ying; Yang, Chenguang

Abstract

© 2019, The Author(s). Real-time grasp detection plays a key role in manipulation, and it is also a complex task, especially for detecting how to grasp novel objects. This paper proposes a very quick and accurate approach to detect robotic grasps. The main idea is to perform grasping of novel objects in a typical RGB-D scene view. Our goal is not to find the best grasp for every object but to obtain the local optimal grasps in candidate grasp rectangles. There are three main contributions to our detection work. Firstly, an improved graph segmentation approach is used to do objects detection and it can separate objects from the background directly and fast. Secondly, we develop a morphological image processing method to generate candidate grasp rectangles set which avoids us to search grasp rectangles globally. Finally, we train a random forest model to predict grasps and achieve an accuracy of 94.26%. The model is mainly used to score every element in our candidate grasps set and the one gets the highest score will be converted to the final grasp configuration for robots. For real-world experiments, we set up our system on a tabletop scene with multiple objects and when implementing robotic grasps, we control Baxter robot with a different inverse kinematics strategy rather than the built-in one.

• Journal Article Type: Article
• Acceptance Date: Sep 30, 2019
• Online Publication Date: Nov 21, 2019
• Publication Date: Feb 3, 2020
• Deposit Date: Nov 22, 2019
• Publicly Available Date: Nov 25, 2019
• Journal: Multimedia Tools and Applications
• Print ISSN: 1380-7501
• Electronic ISSN: 1573-7721
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 79
• Pages: 7427-7446
• DOI: https://doi.org/10.1007/s11042-019-08302-9
• Keywords: Media Technology; Computer Networks and Communications; Hardware and Architecture; Software
• Public URL: https://uwe-repository.worktribe.com/output/4724185
• Publisher URL: https://doi.org/10.1007/s11042-019-08302-9