The Direction Analysis on Trajectory of Fast Neural Network Learning Robot

This study is to efficiently apply artificial neural network (ANN) to the robotics, so as to provide experimental basis for mobile robots to learn the optimal trajectory planning strategy. An algorithm model is innovatively proposed based on back propagation neural network (BPNN) and reinforcement learning (Q-Learning) by combining the motion space, selective strategy, and reward function design. The simulation experiment environment is set and the ROS mobile robot is adopted for simulation experiments. The algorithm proposed in this study is compared with other neural network algorithms from the perspectives of accuracy, precision, recall, and F1. It can be found that the accuracy of algorithm proposed was at least 5.47% higher than that of the model algorithm proposed by other scholars, and the values of precision, recall, and F1 were at least 5.5% higher. The results show that the mobile robot could find the shortest trajectory and the best trajectory in a discrete obstacle environment, no matter the more or less the discrete obstacles or the large or small the space. Therefore, compared to the advanced model algorithms proposed by other scholars in related fields, the robot trajectory planning based on the improved BPNN combined with Q-Learning constructed in this study could realize better results, and can be used in practical applications with robot trajectory planning, providing practical value for the field of machine vision.