High-precision trajectory system of logistics warehouse robots in smart cities

In the construction of smart cities, logistics warehouse robots have become a key tool for improving logistics and warehousing management efficiency due to their efficient and accurate characteristics. To further optimize the logistics workflow and reduce collision and waiting time, a new trajectory prediction model is proposed. The Stanley algorithm and model predictive control module are introduced into the trajectory prediction model, which can help the model predict the direction and distance of the automated guided vehicle in real time, and correct the prediction results. The research results indicated that the trajectory prediction model had good adaptability and accuracy, which accurately predicted various types of motion trajectories. The average deviation of the trajectory prediction model was only 8.65 %, the lowest tracking error was 2.35 m, and the average computation time was 14.15 ms. The trajectory prediction model improved the accuracy of path prediction by 16.05 % compared with traditional long short-term memory network algorithms, with a precision of 0.94. From this, it can be seen that the trajectory prediction model can accurately predict the motion trajectory of automatic guided vehicles. The model proposed by the research institute provides a new tool for path planning of logistics robots, which is helpful for the construction of smart cities. In terms of logistics management, it helps to improve the management efficiency of staff. Management personnel can timely understand the operation status of logistics operations based on the data provided by the model, and make more scientific and reasonable management decisions. The new model can monitor the status of robots in real time, detect wear and tear or abnormalities in advance, and avoid the backlog of goods and interruption of operations caused by equipment failures.