The Research Of Intelligent Navigation And Control Of An Automatic Guided Vehicle

In recent years,many people have researched on AGV (Automatic Guided Vehicle) in many aspects. But the emphasis of their research is trajectory tracking or path following. On navigation,though some papers have proposed a few methods,these methods have many restrictions in application,such as how to avoid collision in a dynamic environment,how to improve the safety and agility,how to improve the precision of navigation using data fusion technology,etc. For the sake of aforementioned reasons,in this paper I take the car-like AGV as my research object,and propose a new navigation method based on safety-measure and a new intelligent control method based on FNN(Fuzzy Neural Net). Furthermore these navigation and control methods are applied to kinematic model and dynamic model in simulation to verify these methods have many fine performances. And I also propose a new method oftracking control. This method divides desired trajectory into many pieces and follows the pieces one by one. With the method,the calculation of curvature of tracking trajectory and complex calculating process can be avoided,and the performance of real-time and precision can be improved. At last,the paper proposes a method of fusing the information of relative location and absolute location for an AGV,which has good performance of high measuring precision. Simulation results demonstrate that the AGV that use the data fusion method has less error of path following than the one that does not use the data fusion method. In order to verify the correctness of the ideas proposed in this paper,we made some experiments,and the results are consistent with those of simulation,so the correctness of our ideas is verified.