Research On Navigation And Location Method Of Sewage Pipeline Robot Based On Machine Learning

With the arrival of the era of artificial intelligence,the field of robotics has ushered in a wave of development.At present,robot technology is rapidly moving towards intelligence,and modern robot technology is no longer just a simple program controlled device,but an "intelligent" machine system with adaptive capabilities.Because humans can rely on advanced visual information to quickly construct scene models in the scene and obtain rich and effective visual information,visual localization is of great significance for robots.This article proposes a multimodal convolutional neural network localization method in the field of machine learning for the navigation and positioning problem of pipeline robots.A simulation model of pipeline robots is built and navigation experiments are conducted on the Robot Operating System(ROS)to verify the effectiveness of the proposed multimodal convolutional neural network localization method.The main research work is as follows:Firstly,in response to the problem of single feature extraction in pipeline scenes,this article uses the Iterative Closest Point(ICP)method to obtain the pose information of pipeline robots while constructing a pipeline scene image dataset.The first image of different specifications of pipelines is used as the initial moment to calculate the pose transformations between other moments and the initial time,and the obtained transformation matrix is used as the text dataset.The text dataset corresponds to the image dataset one by one.Secondly,for the positioning problem of pipeline robots,this article adopts a convolutional neural network based positioning method.Considering that a single pipeline image is not sufficient to locate the position of the pipeline robot,the pose transformation of the pipeline robot has been added as another information for locating the position of the pipeline robot.Based on the above principles,a classification model for multimodal convolutional neural networks was constructed,and comparative experiments were conducted with the single input convolutional neural network classification model.The results show that the positioning accuracy of the single input convolutional neural network model is about 71%,and the loss function is relatively large,while the positioning accuracy of the multi-mode convolutional neural network model is 99.33%,which verifies the effectiveness of the multi-mode convolutional neural network model proposed by us.Finally,in response to the navigation problem of pipeline robots,a pipeline robot and corresponding pipeline scene model were built on Gazebo.Considering that the movement trajectory of the pipeline robot in the pipeline is a straight line,the pipeline robot is considered as a point and the A * algorithm is used for path planning of the pipeline robot.The experimental results show that using the A * algorithm for path planning of pipeline robots can quickly calculate the shortest path,especially in narrow spaces such as pipelines,which can efficiently find the optimal path.