Inspection Robot Design For Oil Storage Transportation Station

The pipelines of oil transportation stations have the characteristics of large flow rate,long route length,high pressure,flammability,explosiveness,and easy environmental pollution.Once abnormal situations occur,if not detected and properly handled in a timely manner,it will pose a threat to the safety of people’s lives and property along the line,as well as the ecological environment,seriously affecting the normal operation of the pipelines.In order to strengthen the safety management of oil pipelines,it is necessary to carry out inspection work,which is currently mainly completed by personnel from oil transmission stations.This repetitive work consumes a lot of manpower and makes it difficult to detect potential risks in a timely manner,posing safety hazards.In view of the above situation,there is an urgent need to improve the inspection technology in oil transportation stations.This article explores the method of replacing manual inspection with robot assisted inspection,and mainly does the following work:Research on Path Planning of Inspection Robots: There are many inspection points within the oil pipeline station,and their safety hazard levels are also different.In order to ensure the shortest inspection path and timely detect problems at inspection points,this article focuses on the path planning strategy of inspection robots in oil pipeline stations.Considering that the robot is in a static obstacle environment and suddenly encounters dynamic obstacles,a hybrid algorithm combining the improved A* algorithm and the improved artificial potential field method is proposed to solve the problem of not being able to avoid dynamic obstacles.This article demonstrates the feasibility of this hybrid algorithm through simulation experiments.It can effectively avoid obstacles,save inspection time,and also improve the quality of path planning.Visual acquisition of inspection data: Traditional instrument data acquisition mainly relies on manual reading,but manual meter reading is hindered by environmental factors such as dust and toxic gases.Moreover,due to subjective factors,there are issues such as misreading,missing readings,and data loss.To reduce reading errors and improve work efficiency,this article studies image processing and machine vision technology to achieve automatic collection of instrument data.To address the issues of blurring,deformation,high light,and low light caused by complex environmental impacts on station instrument images,techniques such as image denoising and image enhancement have been used to greatly improve the quality of instrument images.Using K-nearest neighbor classification(KNN)and coordinate mapping method,based on the characteristics of digital instruments and pointer instruments,the readings are identified to meet the requirements of patrol meter reading.Finally,the feasibility of the above algorithm was demonstrated through experimental verification.Overall plan for inspection robots in oil transportation stations: Traditional technology has limited ability to process data in the development of inspection robots.Therefore,according to the requirements of robot design,a modular design system development concept is adopted,taking into account the existing safety and missed inspection issues in traditional types of chemical inspection,and clarifying the overall design plan and related algorithms of the system.Finally,design hardware and software systems based on the overall plan.