Obstacle Detection And SLAM Path Planning Based On Lidar

Since the 21 st century,mobile robot technology has developed rapidly,and has become the forefront of scientific and technological research,and the research direction is mainly focused on the intelligent control of robot.Using the sensor information carried by the robot to detect obstacles and real-time positioning,mapping and path planning is its core technology.Among all kinds of sensors,lidar has become an important sensor for mobile robot because of its high precision and strong anti-jamming ability.In this paper,lidar is used as the main sensor,combined with a variety of other sensors,to research and test the core technology of the robot.1.Aiming at the obstacle detection technology of lidar,the clustering segmentation technology which is most commonly used in obstacle detection is studied,Combining the advantages of density clustering algorithm and threshold clustering algorithm,an adaptive threshold clustering segmentation algorithm based on distance and obstacle features is proposed.The threshold is adjusted to an adaptive parameter varying with the distance and the density of the obstacle.The algorithm is compared with the traditional clustering segmentation algorithm.2.Aiming at the lidar mapping technology,the mobile robot platform is built,and its coordinate system model and odometer model are established.On this basis,aiming at the problem that the odometer drift of single encoder leads to inaccurate positioning,the extended Kalman filter method is used to fuse the information of encoder and IMU,which can effectively improve the robot positioning accuracy.In the framework of slam system based on particle filter algorithm,the principle of RPBF algorithm and its improved algorithm Gmapping are studied.On the robot platform,the localization and mapping algorithms before and after odometer fusion are compared and analyzed.3.Aiming at the path planning technology of lidar,firstly,the framework of ROS navigation is established,and its map expression and overall process are studied.On this basis,the global path planner with Dijkstra algorithm as the core and the local path planner with DWA algorithm as the core are designed,and the two planning algorithms are simulated and verified by experiments.In order to solve the problem that robots are easy to get close to obstacles and the path smoothness is poor in path planning,a path optimization method based on domain points is proposed,which can effectively improve the navigation efficiency of robots in complex environment.Aiming at the shortcomings of the traditional cruise method,a new cruise method is designed,which can manually adjust the initial pose of the robot and release multiple target points,and can set and output cruise times and time and other parameters.4.Using the built robot platform for experimental verification.Firstly,the clustering segmentation experiments are carried out on indoor and outdoor data,and the improved clustering algorithm is compared with the traditional algorithm.Then,the positioning and mapping experiments are carried out,and the indoor mapping methods before and after odometer fusion are used to analyze and compare the mapping accuracy.Finally,the path planning experiment is carried out.In the complex environment,the designed cruise method is used to compare the path planning methods before and after optimization,and analyze the path and navigation efficiency.70 pictures,4 tables and 63 references.