Intelligent Route Planning Algorithm based on Genetic Neural Network

With the development of modern industry towards large-scale and integration, the production process is becoming more and more complex. The process is seriously nonlinear, time-varying, uncertain and the strong combination of variables, which makes many systems lack accurate mathematical description and difficult to analyze and control with traditional theoretical methods. Therefore, it is necessary to study new intelligent control strategies. Real-time and efficient solution of the optimal path in a large-scale road network is a research difficulty in the field of dynamic path induction. During path planning, the robot's own sensors are required to continuously collect and analyze environmental data, so that the robot can find the target point and update it continuously path. Aiming at the shortcomings of the basic GA, such as low efficiency, when calculating the optimization problems of large-scale networks. In this paper, an intelligent route planning algorithm based on genetic neural network is proposed. The environmental information is obtained by five sensors loaded on the front end. The obtained obstacle, pose and target information are used as the input of neural network, and then the weights of neural network are trained and adjusted by GA. Finally, the output of neural network after training and adjustment is used as the driving control force of robot. On this basis, referring to some conclusions of fixture verification, the stability and deformation characteristics of the workpiece are simulated through some parameters, and the GA is used for combinatorial optimization to determine the optimal positioning point. The algorithm proposed in this paper has the advantages of simple calculation and fast convergence, can avoid some local extremum, and the planned collision free path reaches the shortest collision free path. Finally, through the experimental simulation of the algorithm, the results show that the proposed intelligent route planning algorithm based on genetic neural network is correct and effective. In addition, the real-time performance and rapidity are better than the basic GA, and the balance problem of solving efficiency and solving quality in large-scale road network is also solved.