Affiliations: [a] School of Mechanical Engineering, Shandong University, Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan, China
| [b] Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| [c] College of Mechanical and Electrical Engineering, Organization Qingdao Agricultural University, Qingdao, China
Correspondence:
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Corresponding author. Jun Zhou, School of Mechanical Engineering, Shandong University, Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China. E-mail: zhoujun@sdu.edu.cn.
Abstract: AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments.
