Multi-colony Ant Colony Algorithm Based On Reward And Punishment Mechanism And Its Applications

With the continuous development of mobile robot technology,it can not only improve the efficiency of automation,save the cost of production,but also replace human beings to complete high-risk work in the environment,which is of great significance in military,agriculture,industry and other fields.As an important research direction,path planning technology has attracted the attention of researchers at home and abroad.It is the guarantee for mobile robots to complete tasks from the starting point to the specified target point accurately.In this paper,the ant colony algorithm of classical swarm intelligence algorithm is taken as the research object,and the contradiction between convergence and diversity is solved,and it is applied to the task of path planning.The main work of this paper is as follows:Firstly,an adaptive dynamic chaos ant colony algorithm(DBACS)based on aggregation is proposed to solve the problem of slow convergence and easy to fall into local optimum.In the early stage of the iteration,the diversity of the solution is measured by the degree of convergence,and the local pheromone distribution is adaptively adjusted.At the same time,the chaos operator is introduced to increase the population diversity,so as to avoid the algorithm falling into the local optimum,so as to improve the accuracy of the solution.In the late stage of the iteration,the chaos operator is removed to reduce the chaos disturbance,so as to improve the convergence speed of the algorithm.DBACS is applied to the Traveling salesman Problem(TSP).The simulation results show that this algorithm reduces the search time and improves the quality of the solution compared with the traditional ant colony algorithm.It balances the contradiction between diversity and convergence,and the overall performance is better than the other two algorithms.Secondly,in order to further improve the population diversity and convergence speed,a multi colony ant colony algorithm(PMCACS)based on reward and punishment mechanism is proposed.This algorithm controls the global pheromone update of all kinds of groups through reward and punishment mechanism,and increases the diversity of the population.At the same time,we propose a variety of group interaction methods of pheromone fusion,and use relative entropy and information entropy to control the interaction object and frequency,so as to improve the search ability of the algorithm and promote the fast convergence of the algorithm.The simulation results on TSP show that the algorithm can effectively improve the convergence speed and the quality of the solution,and its performance is better than some traditional efficient multi group algorithms.Finally,the improved multi group algorithm PMCACS is applied to the path planning problem.Verify the effect of the improved ant colony algorithm in different grid environment,compare the traditional ant colony algorithm with the path planning map and convergence curve obtained by this algorithm,the results show that this algorithm has better performance.Using turnlebot2 robot to scan indoor field environment to build environment map,to verify the feasibility of the algorithm in the actual scene path planning.The simulation environment is built with the help of gazebo simulation platform,and the starting point and target point are set up to verify the path planning effect of the robot in the simulation environment.The rolling window algorithm is used to predict the possible conflicts that the robot may encounter,and the MATLAB simulation platform is used to demonstrate the process of avoiding dynamic obstacles,so as to complete the path planning problem under the coordination of multiple robots.