Research On FASTSLAM Algorithm Of Coal Mine Downhole Robot Based On Particle Swarm Optimization

In the coal mine disaster rescue process,due to the coal mine environment is complex and changeable,the use of robots to detect and rescue technology has become the development trend of modern mine,in order to quickly locate the accident location,the position of the robot,timely detect the disaster area environment,in the absence of GPS global positioning system(or cannot use the GPS global positioning system).Area,construction of mobile robot simultaneous localization and map use(SLAM)technology has become a prerequisite for the implementation of effective detection and rescue measures.The rescue robot laboratory of coal science and industry group Chongqing branch of Research Institute for technical support,State Key Laboratory of gas disaster monitoring and emergency technology as the main engineering test platform,using the method of simulation experiment and field experiment combined,using particle swarm optimization algorithm to optimize the original FASTLSAM2.0,improve the algorithm in coal mine environment.Mapping accuracy and efficiency,and the optimized algorithm in coal mine environment performance experiments,the similar simulation experiment closed environment in coal mine,analysis of influence factors of the performance difference algorithm,the main contents include the following aspects:The robot motion model is simplified,and the complexity of the algorithm is improved.In view of the complex environment in underground coal mine,on the map registration method of cloud point scanning laser sensor(ICP),the optimization model is established,and using the penalty function method for solving the optimal solution,adding point cloud map registration accuracy and consistency with the environment.The software of C++is designed,and the effectiveness of the algorithm in the actual working environment is tested and compared.