Design Of AGV Control System And Research Of Navigation Algorithm

In this paper,an Automated Guided Vehicle(AGV)control system is designed according to the requirements of the automation system of logistics and warehousing industry.Each subsystem of the hardware of the control system is designed,mainly including the central control unit,the communication subsystem,the navigation subsystem,the motor subsystem,and the driving subsystem,etc.,and a set of software is designed to realize the AGV driving,turning,positioning,obstacle avoidance and other control.The positioning and navigation of the designed AGV control system is realized by using UWB positioning technology and TDOA ranging algorithm.Through experimental tests that the AGV can run normally and complete the positioning with relatively large errors,the maximum error of the test results is 39 cm,and the MSE fluctuates between 0 and 0.2.In order to improve the positioning accuracy of AGV,the research of AGV positioning algorithm is carried out,including the least square method,Fang algorithm,Taylor algorithm and Chan algorithm.Considering the advantages and disadvantages of the algorithm and the practical application of AGV,Chan algorithm is selected as the positioning algorithm of AGV.Then the Chan algorithm is improved,this paper proposes a Chan algorithm based on adaptive KALMAN filtering,using KALMAN filter algorithm to filter out data contains the system noise and interference,at the same time introducing gauss-Newton iteration to abate the KALMAN filtering algorithm in high order truncation error caused by the linearization of nonlinear system,to improve the filtering accuracy.Finally,the measured values before and after the improved algorithm are compared by experimental tests,the results show that the Chan algorithm based on adaptive KALMAN filter has minimum positioning error,to 12 cm,MSE minimum range of 0-0.03,the location trajectory is more consistent with the real trajectory,the test confirms that the improved algorithm has the feasibility and superiority and can improve the positioning precision of the AGV.The research of AGV navigation is the research of AGV positioning and path planning.The research of AGV path planning is carried out on the basis of the completion of AGV high-precision positioning.Studying traditional ant colony algorithm then finding that the ant colony algorithm falls into the local optimal problem because of some path having large pheromone accumulation in the later stage of searching,this paper proposes an improved algorithm by introducing pheromone enhancement factor and the mutation operators of genetic algorithm,if the ant colony algorithm fails to have a more optimal path selection during the search,it will change the current optimal path,when there is a more optimal path than history,the pheromone concentration of the pathway is increased by pheromone enhancement factor.The limits of pheromone range are set and the search space is expanded to improve the optimization ability of ant colony algorithm and the convergence speed to the optimal solution.According to the established environment model,MATLAB simulation results show that the improved algorithm reduces 2 inflection points of the path and 16 iterations,which proves that the improved algorithm is feasible,has strong optimization ability and fast convergence speed to the optimal solution.