TY - JOUR AB - 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. VL - 47 IS - 2 SN - 0143-991X DO - 10.1108/IR-07-2019-0150 UR - https://doi.org/10.1108/IR-07-2019-0150 AU - Zhang Hui AU - Tan Jinwen AU - Zhao Chenyang AU - Liang Zhicong AU - Liu Li AU - Zhong Hang AU - Fan Shaosheng PY - 2020 Y1 - 2020/01/01 TI - A fast detection and grasping method for mobile manipulator based on improved faster R-CNN T2 - Industrial Robot: the international journal of robotics research and application PB - Emerald Publishing Limited SP - 167 EP - 175 Y2 - 2024/03/28 ER -