Research On Navigation Method Of Drilling Robot For Rockburst Prevention Based On Laser-Vision Fusion

With the development of intelligence in coal mines,intelligentization of coal mine equipment robots is an important development strategy for the country in the future.In recent years,with the continuous increase of coal mine mining depth in our country,coal mine accidents caused by rock burst have also gradually increased,resulting in serious casualties and economic losses.At present,anti-scouring drilling mainly relies on manual operation for drilling pressure relief,but accidents are prone to occur,causing casualties.Therefore,it is of great significance to develop an drilling robot for rockburst prevention with autonomous navigation function to realize the autonomous travel operation of the drilling robot for rockburst prevention,which is of great significance to the prevention and control of rock burst in coal mines and the protection of personnel’s life,health and safety.However,due to the complex environment and harsh working conditions of coal mine roadways,the accuracy and robustness of the navigation method based on a single sensor are low,which cannot meet the application requirements of the coal mine environment.Therefore,this thesis proposes a navigation method for drilling robot for rockburst prevention based on laser-vision fusion to meet the needs of autonomous navigation of drilling robot for rockburst prevention in complex coal mine environments.Carry out research,the main research work and research content are as follows:(1)The framework of drilling robot for rockburst prevention navigation system based on laser-vision fusion is designed.Analyze the actual environment characteristics of the anti-scourr drilling robot’s autonomous travel operation area,put forward the specific requirements of the anti-scourr drilling robot autonomous navigation system,and design the overall framework of the anti-scourr drilling robot navigation system according to the requirements.(2)The SLAM algorithm for drilling robot for rockburst prevention based on laser vision fusion is designed.Research on lidar-based algorithms and vision-based algorithms.Aiming at the problems that a single sensor is affected by the environment in coal mines,it is prone to degradation failure,poor positioning and mapping accuracy,etc.,and designs an algorithm based on laser-vision fusion,and Algorithm comparison experiments are carried out using public datasets to verify the accuracy and effectiveness of the proposed laser-vision fusion algorithm.(3)The global-local path planning algorithm for drilling robot for rockburst prevention is designed.Aiming at the problem of path planning in the static environment of coal mines,the basic theory of global path planning based on the traditional ant colony algorithm is introduced.In order to improve the optimization ability and convergence speed of the algorithm,a global path planning based on the improved ant colony algorithm is proposed.Methods: Aiming at the problem of path planning in the dynamic environment of coal mines,the local path planning based on the algorithm is introduced,and a method of integrating the global path planning of the improved ant colony algorithm and the local path planning of the algorithm is designed,and the algorithm simulation experiment is carried out to verify the superiority of the improved global path planning algorithm and the feasibility of the global-local path planning algorithm.(4)The experimental research on the navigation system of drilling robot for rockburst prevention was carried out.Build an experimental platform,carry out experiments in the simulation environment of Gazebo,the simulated roadway of the State Key Laboratory of Coal Resources and Safe Mining of China University of Mining and Technology,and the experimental base of national key research and development projects,and verify the navigation method based on laser-vision fusion in the coal mine roadway environment Robustness and accuracy under different scenarios as well as stability and effectiveness under different scenarios.In this thesis,there are 88 figures,23 tables and 104 references.