Research On AGV Location And Scheduling Technology Using Two-Dimensional Code Landmark

With the cost rising of labor and the rapid development of mobile robot technology,automated guided vehicle(AGV)has been widely used to achieve operations such as handling in domestic and international warehousing and logistics systems.Mult-AGV collaborative operations can complete complex operation modes,which effectively improve transportation efficiency,shorten production cycle and save operating costs.The scheduling problem in multi-AGV systems is focus on solving the path planning of multiple AGVs,besides,the precise location of AGVs in the map is real-time sensed which is the premise for optimal path planning.Therefore,it has great theoretical significance and application value to study the AGV location technology and multi-AGV scheduling technology.The multi-AGV system applied to warehousing logistics is studied in this paper.Based on the characteristics of two-dimensional code,such as mall occupied area,low cost,large storage information and high recognition rate,the overall scheme of multi-AGV system using two-dimensional code landmarks is proposed.Aiming at the requirements of multi-AGV system,a Java-based PC system is designed and the hardware platform of the car is constructed,which establishes the foundation for the follow-up research of AGV location and multi-AGV scheduling.Because of many problems existing in the AGV visual location research,such as,the position information provided by the two-dimensional code only the discrete positioning of the AGV in the grid-like map realize,and the error of positioning method using Kalman filtering has the accumulated.Monte-Carnot localization algorithm which was integrated with two-dimensional code information was proposed in this paper.Firstly,the cumulative error of the odometer model was improved by absolute position information which was provided by two-dimensional code and then sampling.Then a measurement model provided by laser sensor was used to determine the weights which represent the importance of those particles.Besides,fixed sample set used in the resampling part would cause large amount of calculation.Therefore the Kullback-Leibler distance(KLD)was utilized in resampling to reduce the amount of calculation,And according to the distribution of particles in the state space,the number of particles required for the next iteration has been adaptively adjusted.The AGV is accurately positioned by using the relative position two-dimensional code and camera.The experimental results show that the positioning algorithm can improve the positioning accuracy.In the study of multi-AGV path planning methods,the multi-AGV system has the characteristics of complex path network and large number of AGVs running at the same time,so a complete multi-AGV system path planning solution is proposed.Firstly,the traffic control method is used to solve the AGV conflict problem.Then,the path planning model based on dynamic network is established.And the time factor A* algorithm is used to solve the model,therefore the multi-AGV path planning is realized.Finally,this paper has completed the location of AGV and the test of multi-AGV system,the test result indicates that the method proposed by this paper can complete precise location and the best path planning of multi-AGV,besides it also confirms the stability of the multi-AGV system.