The occurrence of void defects in cement pavement is inevitable and poses a threat to the structural safety of roads. It is urgently necessary to establish an accurate positioning method for void defect areas. Ground-penetrating radar (GPR) was used as a non-destructive testing tool to extract time-frequency features from GPR signals and construct a feature dataset for void defects through forward and inverse experiments. The feature set was then subjected to PCA dimensionality reduction, and SVM and ANN models were respectively established with PCA principal components as input and normal / void output to identify voids. The window energy method was proposed to determine the depth of the void area in the GPR signal of the void. The identification of voids was verified on actual pavement surfaces. The results showed that time-frequency features could characterize void defects, and the subset of data containing parts of the defects was more suitable for modeling and identification. The recognition accuracy of ANN was superior to that of SVM, and the window energy method could effectively locate the depth of the void area and had robustness against noise. In the indoor model, the average positioning error was 1.51%. The research results provide a scientific basis for accurate maintenance of road surfaces.

To meet the short and high frequency demands of quick aircraft towing at logistics airports, a rapid towing method based on unmanned driving technology is proposed. Using the technical route and method of “theoretical modeling-algorithm design-case test and simulation optimization-prototype experiment”, a kinematic model of the towing vehicle is constructed for a 10-ton aircraft towing vehicle, the vehicle’s constraint conditions and control quantities are determined, and the A^* algorithm is improved by adding collision avoidance processing, minimum turning radius, and path smoothing to generate the motion trajectory of the towing vehicle. A trajectory tracking controller based on model predictive control (MPC) is designed, and a joint simulation model of MATLAB/Simulink and ADAMS is constructed. The control parameters of MPC are optimized through trajectory tracking simulation experiments, and trajectory tracking experiments are carried out on the modified electric aircraft towing vehicle prototype. The results show that the improved A^* algorithm meets the requirements of aircraft towing vehicle work path planning and minimum turning radius, the joint simulation method optimizes the MPC controller, achieves good tracking accuracy on the prototype, with standard deviation of tracking errors for curve paths and straight paths being 0.362 m and 0.128 m respectively, realizes the unmanned driving function of the aircraft towing vehicle, and lays the technical foundation for unmanned aircraft towing at smart logistics airports.