Design And Implementation Of Weed Identification And Auxiliary Navigation Module For Substation Inspection Robot

With the acceleration of intelligent construction of substation,substation inspection robot is more and more widely used in substation.Compared with the traditional manual inspection method,the substation inspection robot can reduce the labor intensity of the staff in the station,reduce the risk of safety accidents caused by bad weather,improve the detection accuracy of the equipment,and ensure the accuracy and stability of data collection.The premise of substation inspection robot to complete the inspection task is to have navigation function.At present,the substation inspection robot is mainly equipped with lidar sensor for navigation,which has mature technology and high navigation accuracy.But the substation is an outdoor environment,which will inevitably grow weeds.The navigation mode based on lidar can not identify weeds,which leads to the inspection robot to stop the inspection task,and weeds will not cause danger to the robot.Therefore weeds seriously affects robot inspection efficiency and stability.Therefore,this paper designs an auxiliary navigation module for substation inspection robot navigation,which enables the robot to identify weeds and control them to pass through weeds.At the same time,combined with deep learning neural network method,designed a special network structure for substation weed identification,which effectively improves the accuracy of weed identification,and finally improves the efficiency and stability of the inspection robot.The main contents of this paper are as follows:1.Requirement analysis of auxiliary navigation module for substation inspection robot and data set construction.This paper analyzes the working condition of substation inspection robot in substation environment,summarizes the shortcomings of current navigation mode,and determines the requirements of auxiliary navigation module.At the same time,the weed types to be identified are determined,and the substation weed data set is constructed.2.Improvement of substation weed identification method for substation inspection robot.This paper summarizes and analyzes the advantages and disadvantages of current traditional weed identification methods and deep learning neural network methods,selects a basic research network suitable for substation inspection robot weed identification,and then improves it combined with the characteristics of substation weed data,and finally tests the improved network.The improved method mainly uses the advantages of SSD and Res Net,by using convolution operation instead of full connection operation to locate the target in the image,effectively improving the network’s ability to extract local features,and designs a three-layer residual structure to fuse more abundant local features.Experiments show that the improved network structure can effectively improve the accuracy of weed identification in substation.3.Design and implementation of auxiliary navigation module for substation inspection robot.Combined with the requirements of the auxiliary navigation module,designs the specific scheme of the module.In software,the algorithm of auxiliary navigation module is realized by ROS.in hardware,the parameters of hardware sensors are tested by experiments.Finally,the auxiliary navigation module is transplanted to the inspection robot for testing.The results show that the inspection robot can identify weeds through the auxiliary navigation module,and control the robot to pass through weeds,which improves the efficiency and stability of the inspection robot to perform inspection tasks.