(untitled) (Informatica, 2024)
The logistics industry has made significant progress in recent years. However, there are still issues with low operational efficiency and high costs. Unmanned logistics vehicles have gained attention as an efficient and intelligent mode of transportation with the rapid development of the industry. The study utilizes an advanced path tracking control algorithm, in combination with model predictive control technology, to monitor and adjust the path, speed, and direction of unmanned logistics vehicles in real-time. The aim is to enhance the stability, safety, and efficiency of travel. The experiments revealed that the average accuracy of path deviation prediction of the proposed model on two different datasets is 88.33% and 82.1%, which is 3.96% and 4.72% higher than that of the control model, respectively. The control accuracy of the proposed model reached 94.19% on the KITTI Vision Benchmark Suite dataset and 95.61% on the CARLA Simulator dataset, which are both higher than the other control models. In addition, the study also tested the proposed model for energy consumption, controller switching frequency, lateral error and other indexes, and the findings revealed that the proposed model of the study exhibits high stability and efficiency. This research not only provides new ideas for the control of unmanned logistics vehicles, but also verifies the effectiveness of the control strategy through experiments.