Research On Autonomous Exploration And Obstacle Avoidance Technology Of Mining Search And Rescue Robots

The exploration problem in unknown environments has become one of the most challenging research topics in mobile robot technology,and the research and development of autonomous exploration for ground mobile robots are particularly rapid.Compared to the ground environment,the progress of robot exploration research in underground environments is still slow.Especially for special scenarios such as underground coal mines,the terrain is more complex and the post disaster environment is even worse.In order to efficiently carry out post coal mine disaster rescue operations,it is urgent to develop mining search and rescue robots with high reliability,strong motion performance,environmental perception,and obstacle avoidance capabilities.This thesis will delve into the autonomous exploration and obstacle avoidance capabilities of mining search and rescue robots,propose efficient exploration and reliable obstacle avoidance methods suitable for their application in complex underground environments,and evaluate and verify their performance and effectiveness in simulation and simulation environments.The main research content is as follows:Construction of a mining search and rescue robot platform.Based on the characteristics of the underground environment in coal mines,five requirements for the reliability of robot platform design,chassis mobility,communication ability,endurance,and human-machine interaction ability are proposed.Develop a mining search and rescue robot platform suitable for post disaster coal mines,and design and select robot control systems,perception systems,and communication systems.Based on the robot operating system(ROS),the human-computer interaction interface is developed,the interface functions are analyzed,and the kinematics of the tracked robot is solved.Research on Efficient Autonomous Exploration Algorithm Based on Improved Dual Stage Viewpoint(DSVP＿Change).Based on the analysis of the autonomous exploration algorithm framework,the SLAM algorithm suitable for underground coal mines is studied,and a point cloud map terrain segmentation method is proposed.An analysis was conducted on the exploration and relocation stages of the original DSVP method,and improvement strategies were proposed based on its shortcomings and usage environment.Finally,an underground mining cave scene and a tracked simulation robot were constructed in the Gazebo environment to verify the performance of DSVP,DSVP＿Change,NBVP,GBP,and MBP.The results show that the exploration efficiency of DSVP＿Change has been improved by 32% compared to DSVP,and other methods cannot fully cover the simulation environment,verifying the efficient exploration ability of the improved algorithm.Research on Autonomous Obstacle Avoidance Algorithm Based on Improved Nonlinear Model Predictive Control(NMPC＿OP).Firstly,the geometric path after linear interpolation is regressed and predicted by Gaussian process,and the feasibility of dense path is obtained.Then,according to the kinematics model of the crawler robot and the ground analysis results,a prediction model suitable for fitting the plane NMPC is established,and a nonlinear optimization function is designed to predict the specific position and pose of the robot model.By accurately estimating the shape and position of the obstacle itself and changing the constraint conditions of the nonlinear function,it has the ability to avoid dynamic and static obstacles.Finally,compare the adaptability of NMPC＿OP,MPC,and FALCO algorithms to different scenarios in the simulation platform.Simulation experiments have shown that the average planning efficiency of NMPC＿OP has increased by 8.43% compared to FALCO and 12.35% compared to MPC,indicating that the designed NMPC＿OP obstacle avoidance method can plan smoother obstacle avoidance routes in a shorter time.On site experimental verification of autonomous exploration and obstacle avoidance algorithms for mining search and rescue robots.Use ROS tools to calibrate the radar odometer,and use total station to verify the accuracy of mapping,providing guarantee for subsequent tests in the real environment.Then,multiple experiments were conducted on the DSVP and DSVP＿Change algorithms in real coal mine underground simulation environments and campus environments.The results show that the exploration efficiency of DSVP＿Change has increased by 17% compared to DSVP,and it has stronger robustness and exploration efficiency.Multiple sets of obstacle avoidance scenarios were set up in the underground environment to validate the obstacle avoidance algorithm.Among the four scenarios,the average planning efficiency of NMPC＿OP improved by 23.49% compared to FALCO,indicating that the proposed NMPC＿OP can complete the planning of obstacle avoidance routes in a shorter time and has very excellent obstacle avoidance performance.This thesis has 99 images,19 tables,and 96 references.