Aiming at the problem of neglecting some narrow roads due to the formation constraints in the multi-UAV (Unmanned Aerial Vehicle) cooperative trajectory planning, a Fast Particle Swarm Optimization method based on Adaptive Distributed Model Predictive Control (ADMPC-FPSO) was proposed. In the method, the formation strategy combining leader-follower method and virtual structure method was used to construct adaptive virtual formation guidance points to complete the cooperative formation control task. According to the idea of model predictive control, combined with the distributed control method, the cooperative trajectory planning was transformed into a rolling online optimization problem, and the minimum distance and other performance indicators were used as cost functions. By designing the evaluation function criterion, the variable weight fast particle swarm optimization algorithm was used to solve the problem. The simulation results show that the proposed algorithm can effectively realize the multi-UAV cooperative trajectory planning, can quickly complete the adaptive formation transformation according to the environmental changes, and has lower cost than the traditional formation strategy.
The teaching of Chinese architecture history has building structures too complex, is limited to 2D planar teaching and is not easy for students to master and apply, therefore an implementation method of Chinese architecture history teaching system based on mixed reality technology was proposed. The wooden structure system of Baoguo Temple in Ningbo was taken as an example, and the mixed reality device Microsoft HoloLens was used as the teaching platform. Firstly, 3ds Max was applied to the 3D simulation modeling of the wooden structure system of Baoguo Temple based on the collected data, and a building model library was built. Then, the 3D human-computer interface of the virtual teaching system was constructed in unity3D, the key technologies were used including environment understanding and human-computer interaction based on C# scripts, and a Chinese architectural history teaching system using HoloLens was implemented with core functions of building structure recognition and cultural cognition. The results show that the system has good 3D visual effects and natural effective human-computer interaction, which can improve the efficiency of knowledge transfer and the initiative of students.
To deal with poor robustness in strong noise environment, weak adaptability to complex mixed noise that appear in the existing unsupervised image segmentation models, an improved noise-robust image segmentation model based on One-class SVM (Support Vector Machine) method was proposed. Firstly, a data outlier detection mechanism was constructed based on One-class SVM. Secondly, an outlier degree was introduced into the energy function, so that more accurate image information could be obtained by the proposed model under multiple noise intensities and the failure of weight-descend mechanism in strong noise environment was avoided. Finally, the segmentation contour was driven to the target edge by minimizing the energy function. In noise image segmentation experiments, the proposed model could obtain ideal segmentation results with different types and intensities of noise. Under F1-score metric, the proposed model is 0.2 to 0.3 higher than LCK (Local Correntropy-based K-means) model, and has better stability in strong noise environments. The segmentation convergence time of the proposed model is only slightly longer than that of LCK model by about 0.1 s. Experimental results show that the proposed model is more robust to probabilistic, extreme values and mixed noise without significantly increase of segmentation time, and can segment natural images with noise.