Grasping detection of dual manipulators based on Markov decision process with neural network

Yun, Juntong; Jiang, Du; Huang, Li; Tao, Bo; Liao, Shangchun; Liu, Ying; Liu, Xin; Li, Gongfa; Chen, Disi; Chen, Baojia

Abstract

With the development of artificial intelligence, robots are widely used in various fields, grasping detection has been the focus of intelligent robot research. A dual manipulator grasping detection model based on Markov decision process is proposed to realize the stable grasping with complex multiple objects in this paper. Based on the principle of Markov decision process, the cross entropy convolutional neural network and full convolutional neural network are used to parameterize the grasping detection model of dual manipulators which are two-finger manipulator and vacuum sucker manipulator for multi-objective unknown objects. The data set generated in the simulated environment is used to train the two grasping detection networks. By comparing the grasping quality of the detection network output the best grasping by the two grasping methods, the network with better detection effect corresponding to the two grasping methods of two-finger and vacuum sucker is determined, and the dual manipulator grasping detection model is constructed in this paper. Robot grasping experiments are carried out, and the experimental results show that the proposed dual manipulator grasping detection method achieves 90.6% success rate, which is much higher than the other groups of experiments. The feasibility and superiority of the dual manipulator grasping detection method based on Markov decision process are verified.

• Journal Article Type: Article
• Acceptance Date: Sep 7, 2023
• Online Publication Date: Sep 14, 2023
• Publication Date: Jan 31, 2024
• Deposit Date: Jan 2, 2024
• Publicly Available Date: Sep 15, 2025
• Journal: Neural Networks
• Print ISSN: 0893-6080
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 169
• Pages: 778-792
• DOI: https://doi.org/10.1016/j.neunet.2023.09.016
• Public URL: https://uwe-repository.worktribe.com/output/11473445