Research On Path Planning And Control Of The Ground Robot

As a new type of artificial intelligence,ground robot is more and more popular,and AGV(automatic guidance vehicle)which belongs to ground robot is widely used in logistics storage system.The running path of AGV has a direct impact on its transportation efficiency.In order to further improve the economic benefits of logistics warehousing,improving the key technology is the most important.Therefore,this paper mainly solved two key problems: AGV's path planning and control research.In the aspect of path planning,aiming at the shortcomings of slow convergence speed and easy to fall into local optimum in AGV vehicle path planning under complex environment,this paper proposed a geometric rule-based heterogeneous ant colony optimization(GR-HFACO)algorithm.Firstly,geometric rules were used to distribute initial pheromones nonuniformly,and a two-way parallel search mechanism was set up.Secondly,ant cooperative work with viewpoint selection ability was introduced.Finally,pheromone negative feedback link and cross-operation were introduced into update link,and the global convergence of GR-HFACO algorithm was proved mathematically..Simulation results showed that the convergence speed and global search performance of the algorithm were significantly better than the popular ACON,TWPSS-ACO,SoSACO-v2,Sci-ACO and HHACO algorithms.In the aspect of control research,aiming at the trajectory tracking control of AGV,a fuzzy controller was designed based on the fuzzy control principle.Through the analysis of the kinematics model of AGV,two variables controlling the position and attitude change of AGV in absolute coordinate system were obtained,and these two variables were taken as output variables to realize the trajectory control of AGV,and the simulation results with MATLAB were satisfactory.