A fast detection and grasping method for mobile manipulator based on improved faster R-CNN. Issue 2 (3rd February 2020)

• Record Type: Journal Article
• Authors: Zhang, Hui; Tan, Jinwen; Zhao, Chenyang; Liang, Zhicong; Liu, Li; Zhong, Hang; Fan, Shaosheng
• Is Part Of: Industrial robot. Volume 47:Issue 2(2020)
• Page Start: 167
• Page End: 175
• Publication Date: 2020-02-03
• Subjects: Grippers -- Robotics -- Machine vision -- Robot vision -- Automated guided vehicles (AGV) -- Improved faster R-CNN -- DACAB -- Object detection -- Mobile manipulator; Robots, Industrial -- Periodicals; Machinery in the workplace -- Periodicals; 629.892
• DOI: 10.1108/IR-07-2019-0150
• Languages: English
• ISSNs: 0143-991X

Abstract

Abstract : Purpose: This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf. Design/methodology/approach: To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method. Findings: The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully. Originality/value: The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.