Title: Automatic control method of driving direction of unmanned ground vehicle based on association rules
Authors: Min Yang; Zhuqiao Ma; Longyu Cai
Addresses: School of Intelligent Manufacture, Nanjing University of Science and Technology Zijin College, Nanjing 210046, China; School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210042, China ' School of Intelligent Manufacture, Nanjing University of Science and Technology Zijin College, Nanjing 210046, China ' School of Intelligent Manufacture, Nanjing University of Science and Technology Zijin College, Nanjing 210046, China
Abstract: In order to overcome the problems of large position deviation and angular deviation in the traditional driving direction control method of unmanned ground vehicles, an automatic driving direction control method of unmanned ground vehicles based on association rules is proposed. First, the Apriori algorithm is used to find frequent itemsets of unmanned ground vehicle driving data, generate association rules, and determine the association between itemsets in unmanned ground vehicles (UGV) driving data. After determining the data attributes, the decision tree algorithm is used to complete the mining of UGV driving data. According to the mining results, the proportion integral differential (PID) feedback control algorithm is used to obtain the steering wheel angle control input required for trajectory tracking, and the automatic control of the driving direction is completed. The experimental results show that in various complex traffic environments, the method can control the position deviation of the unmanned ground vehicle between -0.28 m and 0.43 m, and the angle deviation between -0.21 and 0.23 degrees to minimise the deviation. The method in this paper has certain application value and is worthy of further promotion and application.
Keywords: association rules; unmanned ground vehicle; driving direction; automatic control.
International Journal of Vehicle Information and Communication Systems, 2022 Vol.7 No.4, pp.350 - 365
Received: 05 Nov 2021
Accepted: 23 Feb 2022
Published online: 15 Feb 2023 *