An autonomous navigation method based on improved convolutional neural network for a quadrupedal robot for cable tunnel inspection task

The conventional quadruped robot cable tunnel inspection task autonomous navigation recognition mode is preset as vertex processing. The complexity of navigation is high, which leads to the prolongation of the average time of autonomous navigation. Therefore, the design and verification analysis of the quadruped robot cable tunnel inspection task autonomous navigation method based on improved convolutional neural network are proposed. According to the current autonomous navigation mode, first deploy autonomous patrol navigation nodes to collect patrol navigation data, adopt the circulation recognition mode, simplify the navigation complexity, and add automatic circulation recognition program. On this basis, an improved convolutional neural network quadruped robot autonomous navigation model for cable tunnel inspection task is constructed, and the displacement correction adjustment method is used to achieve autonomous navigation processing. The test results show that for the six selected cable tunnel patrol navigation test sections, the average time of autonomous navigation finally obtained at the three selected navigation points is controlled below 0.25s, which indicates that the autonomous navigation method of the quadruped robot cable tunnel patrol task designed by combining the improved convolutional neural network is more flexible, specific and targeted, In the patrol navigation project, the response ability is faster and has practical application value.