Research On The Control Of Autonomous Driving Of Intelligent Scraper

Mineral resources are important material basis for developing national economy and safeguarding national security.With the increasing demand for mineral resources,the mining work gradually turns to the deep underground.Harsh mining conditions have also increased the threat to miners' safety.Because of its outstanding advantages of high efficiency,flexibility,maneuverability and multi-purpose,the scraper has become the preferred equipment for underground efficient mining.With the rapid development of intelligent mine technology in recent years,the scraper is gradually moving towards intelligent development.Autonomous driving technology is one of the core technologies of intelligent scraper.Path planning and path tracking are two important areas in the autonomous driving technology of the intelligent scraper,which also need to be studied and solved.By referring to a large number of relevant literature and materials,this paper understands the background significance and research status of intelligent scraper.The realization methods and related algorithms of autonomous driving of intelligent scraper are summarized.In this paper,the autonomous driving technology of intelligent scraper is deeply studied.The main contents are as follows:Firstly,the global path planning of the intelligent scraper based on the improved RRT algorithm is researched.Aiming at the shortcomings of traditional RRT algorithm and the self-constraint of intelligent scraper,an improved RRT algorithm is proposed.The adaptive step size strategy,sampling point selection strategy,minimum turning radius constraint,minimum safe distance constraint and path smoothing strategy are designed.The simulation results show that the improved RRT algorithm can effectively plan the feasible path of the intelligent scraper.Compared with the similar algorithm,the path planned by the improved RRT algorithm is more suitable for the actual operation of the intelligent scraper,and it is advanced in reducing the path length and avoiding the path twists.Secondly,the local path adjustment method of intelligent scraper based on environment perception is researched.By studying and analyzing the method of environment perception,the laser radar can realize the environment perception of the working area of the scraper and obtain the distance information of obstacles or tunnel walls.Based on the environment perception,the local path planning methods of advanced ranging strategy,bilateral center strategy and single edge wall strategy are designed.Simulation results show that the proposed method can realize local path adjustment of the intelligent scraper.Thirdly,the path tracking of intelligent scraper based on improved Pure Pursuit algorithm is studied.Aiming at the shortcomings of traditional Pure Pursuit algorithm,an improved Pure Pursuit algorithm was proposed.An adaptive preview distance strategy is designed.A temporary preview point is selected to calculate the current course Angle and course deviation Angle,and the preview distance is adaptively adjusted according to the course Angle and course deviation Angle.The tracing path is adjusted by constructing two tangent circles.The simulation experiments of straight expected path tracking and curve expected path tracking were carried out respectively.The experimental results show that the improved Pure Pursuit algorithm can achieve path tracking.Compared with the similar algorithm,the displacement error and heading error are reduced,and the tracking accuracy is higher.Finally,the control system of the intelligent scraper is realized.The traditional scraper was refit,the environment sensing module,the state sensing module and the core control module were installed,and the network communication system was designed.The autonomous driving test of the intelligent scraper was carried out in Dayingezhuang gold mine and good results were obtained.