Development Of Indoor Service Robot Based On ROS And Laser SLAM

As an important tool for guiding visitors into the exhibition hall,service robots need to have the capacity to achieve accurate positioning,navigation and dynamic obstacle avoidance in the face of complex environments and constantly moving visitors.For the limitations of non-intelligent guidance robots such as traditional indoor fixed wiring,and the problem of position misalignment due to the occurrence of wheel slippage and accumulated errors.The article is composed of a Robot operating system(ROS)based on 2D Li DAR for Simultaneous localization and mapping(SLAM)and automatic navigation to solve the problem of localization and navigation in unknown indoor environments.The aim is to develop an indoor Exhibition Hall service robot with environmental map construction and autonomous navigation.The following work is carried out in this paper.(1)By designing the overall architecture for autonomous navigation of the exhibition hall service robot,the overall architecture is divided into two parts: platform building and control system.The hardware architecture of the robot experimental platform is designed and the related hardware is selected,while the mathematical models of Li DAR,kinematics and odometry are analyzed.(2)The robot control system is investigated,and the software architecture is based on the ROS robot operating system for software development and multi-machine distributed transmission,while the robot simulation environment and model are built.Dividing the whole control system into driver layer,sensing layer and control layer.The driver layer mainly performs the data interaction between ROS and the driver layer and the driver control of the robot platform.In the sensing layer,the LIDAR drive control and the data processing of odometer information and inertial measurement unit(IMU)are carried out,and for the most critical control layer,the extended Kalman filter(EKF)is used to fuse the IMU and odometer data for the problem of wheel slippage and idling or cumulative error in long time operation,which leads to robot position inaccuracy.For the most critical control layer,the data fusion of IMU and odometer is carried out by using extended Kalman filter,and the core functions such as environment map construction and path planning are built,and the cost map is introduced,as well as the implementation of multi-point cruise combined with speech synthesis to complete the cruise broadcast function of the exhibition hall is proposed.(3)Three laser SLAM algorithms including Gmapping,Hetcor and Cartographer algorithms are studied and analyzed,and the autonomous localization algorithm and the path planning algorithm of the Exhibition Hall service robot are studied.For autonomous robot localization,the Adaptive Monte Carlo Localization(AMCL)algorithm is applied.For path planning,the A* algorithm is chosen for global path planning and the Dynamic Window Approach(DWA)algorithm is used for local path planning.(4)The joint simulation of Rviz and Gazebo was used to verify and analyze the functions and compare them,and a more suitable Cartographer SLAM algorithm was selected to map the Exhibition Hall environment.After the system debugging,the sensor data fusion experiment and analysis were conducted.In the sensor data fusion experiment,the actual running trajectory of the service robot and the estimated trajectory were more suitable after fusion,and the expected effect was achieved.The average position deviation and standard deviation(SD)of the robot from the target point were less than 7 cm and 3 cm,respectively,when the robot moved at a speed of 0.5 m/s.The average heading deviation was less than 9° and SD was less than 3°.The SD is less than 3°.The positioning and navigation of the robot can meet the requirements of the pavilion service robot well.