Application Research Of Adaptive Simulated Annealing Polymorphic Ant Colony Optimization Algorithm To Mobile Robot Path Planning

With the rapid development of intelligent science,mobile robots are not only used in industry,agriculture and other fields,but also widely used in people's social life to help people solve complex and dangerous social work.Although different kinds of mobile robots work in different content,in order to meet the characteristics of efficient and convenient in the working environment,each kind of robot needs to design the corresponding path planning route according to the actual environment.Therefore,facing the characteristics of different path environment,how to apply reasonable path planning technology has become the focus of many researchers.At present,common path planning techniques are divided into traditional mathematical methods and bionic intelligence methods represented by ant colony optimization algorithm.Among them,ant colony optimization algorithm is the most widely used and can be combined with other algorithms to solve optimization problems.In the process of path planning,ant colony optimization algorithm shows the characteristics of easy to understand,strong operability,positive feedback mechanism,and is conducive to distributed parallel computing.In the process of the optimal path planning,however,it is easy to appear slow convergence speed and falling into the most superior condition.In this paper,combining the idea of ant division and classification in polymorphic ant colony optimization algorithm and the Metropolis criterion in simulated annealing algorithm,an adaptive simulated annealing polymorphic ant colony optimization(ASAPACO)algorithm is proposed to apply to the path planning process of mobile robot.The main research contents are as follows:1.Introduced several common mobile robot environments modeling methods and path planning methods,and described and analyzed the application scope,advantages and disadvantages of different methods and research status.2.Conduct relevant experimental simulation analysis for the application of ant colony optimization algorithm in the path planning of mobile robots,including the influence of the number of ants,the proportion factor of pheromone trajectory intensity,the proportion factor of illumination degree,the volatile factor of pheromone,and the complexity of the environment on the operation results and convergence of the algorithm.Several directions of robot path planning which need to be improved by ant colony optimization algorithm are summarized.3.The design idea of polymorphic ant colony optimization algorithm and Metropolis criterion of simulated annealing algorithm are introduced.On the basis of polymorphic ant colony optimization algorithm,Metropolis criterion is used to improve the state transfer mechanism.At the same time,the influence of pheromone concentration on the algorithm was obtained through prior knowledge,and a dynamic adaptive pheromone updating mechanism was designed to make the pheromone concentration adaptively change with the algorithm evolution process,and improve the optimization convergence speed of the proposed algorithm ASAPACO.4.The proposed ASAPACO algorithm was applied to the tourism route planning process of Jingdezhen Ceramic cultural Scenic spot,and the planned path was compared with the mapping value of Google map to further verify the actual effectiveness and feasibility of the proposed algorithm.