To optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi-objective flexible job shop scheduling model with the objective functions of minimizing the maximum completion time, processing cost, and carbon emissions generated from processing. The ASA-NSGA-EE algorithm adopts an adaptive crossover and mutation genetic strategy, which dynamically adjusts the crossover and mutation rates based on the evolutionary stage of the population, aiming to reduce the loss of optimal solutions. Additionally, it incorporates the simulated annealing algorithm to optimize the selection strategy by leveraging its cooling characteristics. Furthermore, it improves the elite strategy through incorporating elite selection criteria. Finally, by simulation experiments, the effectiveness of the improved NSGA-II algorithm is validated by comparing it with other algorithms.

In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5–69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

With the change of manufacturing mode and the progress of science and technology, the traditional manufacturing industry has gradually developed to intelligent manufacturing and flexible manufacturing. To achieve factory transformation, Automatic Guided Vehicle (AGV) is indispensable. In this paper, an AGV trackless guidance technology based on global vision is proposed. Firstly, the global vision camera is used to obtain the image of the AGV driving area, and then the obstacle information and position information of the AGV are obtained by image processing technology. Secondly, the A* algorithm is used to intelligently plan the AGV driving path, and the wireless network communication is used to control the AGV driving according to the planned path. Experiments show that the method is feasible and has the advantages of high flexibility, high precision, low cost and strong expansibility, which is of great significance to the realization of intelligent warehouse and unmanned chemical plant.

In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabilistic Roadmap algorithms (PRM). The improved algorithm incorporates grid-based processing for map segmentation, employing non-uniform probability density sampling based on the different attributes of each block to enhance sampling probability in narrow passages. Additionally, considering the computational cost and frequent ineffective searches in traditional PRM algorithms during pathfinding, this paper optimizes the time required for query route planning by altering connection strategies to improve the algorithm’s runtime. Finally, the simulation results indicate that, with a reduction of over 50% in undirected line segments and a reduction of over 85% in runtime, the GN-PRM algorithm achieves a 100% success rate in complex planning scenarios with a sampling point value of K = 500. In comparison, the traditional PRM algorithm has a success rate of no more than 10%, with a sampling point value of K = 500.

Pedestrian protection technology has become the most important issue in the field of vehicle safety. The design and simulation method of pedestrian protection system for vehicles is studied based on the Teoriya Resheniya Izobreatatelskih Zadatch theory in this article. The optimal solution of a new type of all-around airbag system mounted to the front of vehicles is presented after performing some Teoriya Resheniya Izobreatatelskih Zadatch analysis tools, such as the component analysis, causal analysis, resource analysis, and contradiction analysis. The digital modeling is carried out on the airbag deployments by SolidWorks, and the simulation analysis is performed on the collision between vehicle and pedestrian head by HyperWorks and LS-DYNA. The results show that the vehicle equipped with the front-end all-around airbag than without can play a better role in pedestrian head protection during the collision, making the maximum value of head injury criterion decrease by 66.7%, effectively reducing the collision damage and verifying the feasibility of the solution.