Research On Cooperative Adaptive Cruise Control Strategy With The Influence Of Traffic Flow

With the development of mobile chassis technology,autonomous navigation chassis are increasingly used in home furnishing,medical care,rescue and industrial production,and promote the development of social productivity.However,in the complex environment of the factory,the non-omnidirectional wheel chassis is limited by its motion model,and the application effect in narrow indoor spaces is not ideal.At the same time,in terms of navigation,the traditional mobile chassis has a fixed track and low intelligence.Therefore,the research on autonomous navigation of mobile chassis has become a current research hotspot.Therefore,this article focuses on the indoor factory environment and studies the autonomous navigation technology of the omnidirectional mobile chassis.The main research work of this paper is as follows:Firstly,the research status of mobile chassis,navigation technology and path planning are studied,and the omnidirectional navigation chassis is modeled systematically.The software system based on the ROS navigation framework and the hardware system based on sensors and processors are introduced.Aiming at the Mecanum wheeled omnidirectional chassis,the forward and inverse kinematics model of the mobile chassis used in this paper is established.The research includes the odometer model and laser sensor model of the radar model,and the coordinate system conversion model used in the navigation system is introduced.Secondly,use the omnidirectional wheel chassis to perform SLAM mapping of the indoor environment.The SLAM problem model is described.For the specific sensor configuration of the omni-wheel chassis,two applicable mainstream SLAM mapping algorithms are analyzed and compared,and the more appropriate Gmapping algorithm is selected as the mapping algorithm in the navigation system.The principle of the Gmapping algorithm is explored.Aiming at the characteristics and error of the chassis platform sensor configuration used in this paper,the number of particles used in the algorithm and the scanning matching threshold Score are improved to improve the accuracy of mapping,and the mapping experiment is carried out to verify the effect of the improved Gmapping algorithm.Then,the global planning algorithm of the autonomous navigation system is researched and improved.Introduced the rapid search random tree(RRT)algorithm and its mainstream variant algorithms.Aiming at the problems of the RRT algorithm,this paper combines the target-biased RRT and the progressively optimal RRT* to propose the G-RRT* algorithm to solve the slow convergence speed of the RRT algorithm,the shortcomings of non-optimal paths;proposed expansion processing ideas to solve the problem of paths too close to obstacles;proposed path pruning ideas to solve the problem of too many redundant points;proposed curve smoothing ideas to solve the problem of tortuous paths.Finally,the feasibility,superiority and robustness of the improved RRT* algorithm proposed in this paper are verified through multiple sets of simulation comparison experiments.Finally,the navigation design and experiment of the omnidirectional wheel chassis are carried out.The omnidirectional mobile experimental platform used in this article is introduced,the navigation system framework of this article is designed,and the parameter configuration of the navigation system based on ROS and the writing of improved algorithms are completed.Through the positioning experiment and the autonomous navigation experiment in the environment of obstacles,obstacles and dynamic obstacles,the feasibility and robustness of the omnidirectional wheel chassis autonomous navigation system proposed in this paper are verified.