Research On Indoor SLAM And Path Planning Of Omnidirectional Mobile Robot

With the increasing development and breakthrough of artificial intelligence and sensor technology,mobile robots have been widely applied in various fields of production and life.As the core technology of mobile robot,autonomous navigation provides the robot with perception-based path planning ability,which is the core technology for mobile robot to realize autonomous and intelligent.However,in the narrow and complex indoor environment,the navigation efficiency of the traditional chassis robot is often low,while the mobile robot based on Mecanum wheel can achieve omni-directional unconstrained movement,with good mobility and flexibility.Therefore,this thesis investigates the autonomous indoor navigation technology for mobile robots based on the Mecanum wheel structure.The main work of this paper is as follows:Firstly,the omnidirectional mobile robot system is analyzed and modeled.The kinematics model of the Mecanum wheeled mobile robot,the trajectory estimation model based on odometer,and the lidar observation model are established.The environmental map representation methods are analyzed and selected,which provid a theoretical basis for subsequent research.Secondly,Simultaneous Localization and Mapping(SLAM)is investigated to solve the problem of map construction for mobile robots in unknown environments.Based on the description of the mathematical problem of SLAM,the basic principles of filter-based and graph-optimization-based SLAM algorithms are introduced respectively,with emphasis on indepth research of their typical Gmapping SLAM and Cartographer SLAM algorithms.While discussing their principles,detailed mathematical derivation and algorithm flow are provided,and the algorithm is deployed to the mobile robot terminal based on the Mecanum wheel.The two algorithms are applied to experimental verification in the same environment.Through comparative analysis of the constructed maps,the Cartographer SLAM algorithm is ultimately selected to construct the environment map,providing the robot with an initial static map for autonomous navigation.Then,in order to solve the path planning problem of mobile robots in the constructed SLAM map,the path planning algorithm for mobile robots is studied.In the process of global path planning,the traditional A* algorithm has long search time in the process of global path planning,and the planned paths have many turning points and symmetry,especially in the large scene environment with low search efficiency.This thesis present an improved A*algorithm,which is improved by introducing the idea of jump point search algorithm and optimizing the search strategy,and using the cubic B-spline method is used to smooth and optimize the generated path.The simulation results show that the improved A * algorithm can expand fewer nodes while generating the optimal path,with faster path finding speed,and the acceleration effect becomes more obvious with the increase of the environment map.At the same time,the Time Elastic Band(TEB)algorithm is used for local path planning to address the influence of obstacles in the environment on navigation,the principle of the algorithm is introduced,and the effect of obstacle avoidance is verified in the simulation.Finally,to verify the practical operation of the proposed algorithm,the omnidirectional mobile robot indoor navigation and obstacle avoidance experiments are conducted by synthesizing the previous studies.The ROS-based Mecanum wheel omnidirectional mobile robot navigation platform is designed and built to complete the creation of an environment map in a realistic environment as a static map for navigation,and the experiments on autonomous navigation and obstacle avoidance are conducted in static and dynamic environments,respectively.The experimental results demonstrate that the robot can rapidly plan an optimal driving route,avoid obstacles while traveling,and reach the target point safely and efficiently,which verifies the effectiveness of the presented algorithm and the feasibility of the built navigation system in practical applications.