Reserch On Autonomous Localization And Navigation System For AGV

With the needs of automation,information,and intelligent development in manufacturing and warehousing and logistics industries,the role of Automated Guided Vehicle(AGV)has become more and more important,and they have been widely used in various workplaces,while autonomous navigation technology is an important manifestation of AGV intelligence.Sensors are the "eyes" of robots to perceive the world which play a very important role in the robot control and make the robot have human-like response capabilities.Thus,How to use the muiti-sensor information integration technology efficiently is becoming more and more important.Therefore,the research of AGV localization and autonomous navigation system based on information fusion has important theoretical significance and application value.The main works of this thesis are as follows:Firstly,the information fusion technology,AGV and navigation technology were studied.The robot operating system(ROS)is selected as the system software platform,and photoelectric encoders,angle sensors,laser range finder,Kinect sensors.are used as the equipments of.the structure of AGV localization and navigation system based on information fusion is designed.Secondly,in the study of AGV particle filter SLAM,the basic research on the traditional particle filter is carried out at first.the traditional particle filter Simultaneous Localization and Mapping(SLAM)often uses single sensor.Aiming at the problem of poor anti-interference,low reliability and large errors.This thesis proposes an Rao-Blackwellized Particle Filter SLAM map construction method based on Bayesian method combined with Kinect sensor and laser sensor.The information fusion at the decision-making level of SLAM improves the accuracy of AGV localization and the accuracy of grid map construction.The Bayes method combines the Kinect sensor and the particle filter SLAM method of the laser sensor to perform information fusion at the decision layer of the SLAM to improve the accuracy of the AGV localization and the accuracy of the grid map construction.Thirdly,Aiming at the problem of Cubature particle-based Monte Carlo Localization(MCL)algorithm with high complexity and slow operation speed,a adaptive iterative particle MCL(AICMCL)algorithm is proposed.The Gauss-Newton iterative update is used to reduce high-order truncation errors in the Cubature particle filter and improve its accuracy.In addition,the Kullback-Leibler Distance(KLD)method is selected for the AICMCL algorithm.According to the distribution of the predicted particles in the free space,the number of on-line particles is adjusted to reduce the calculation cost and improve the quality of probability density function.Finally,the effectiveness and accuracy of the improved algorithm are verified by simulations and experiments.Finally,the design and construction of AGV localization and navigation system based on information fusion under the ROS platform is completed,including the hardware platform and the software platform based on embedded real-time operating system.The localization and navigation system proposed in this thesis is verified in multiple environments.The experimental results show that the overall solution of AGV localization and autonomous navigation system based on information fusion is feasible and reliable.