Research On Mobile Robot Path Planning Based On Ant Colony Algorithm With Electrostatic Potential Field

With the development of science and technology and the progress of the times,mobile robots are not only widely used in the industrial field,but also play various important roles in people's daily lives.As an important part of mobile robot navigation tasks,path planning has attracted the attention of many scholars.The ant colony algorithm is more conducive to distributed computing due to its parallelism,and has high robustness.The structural characteristics with heuristics make it easy to combine with other methods.These advantages make it have certain advantages in solving path planning in complex environments.However,the ant colony algorithm still has the problems of low search efficiency,search stagnation,and easy to fall into the local optimal solution during the optimization process.In view of the above problems,this paper proposed an improved ant colony algorithm combined with the electrostatic potential field method.The details are as follows:1.On the basis of analyzing the principles of typical classical ant colony algorithm,three pheromone updating models are simulated and analyzed with the help of the traveling salesman problem,and the ant-cycle model is determined to be used in the path planning problem.Three typical improved algorithms and their pheromone updating methods including the improved ant colony algorithm with elite strategy are discussed.2.Several environmental modeling methods in path planning are introduced,and the characteristics of different modeling methods are compared.The implementation,advantages and disadvantages of ant colony algorithm in path planning are analyzed.The influence of population number,heuristic influence factor and pheromone influence factor on algorithm performance is analyzed by simulation.The characteristics of genetic algorithm and ant colony algorithm in path planning are compared by simulation.3.The principle of electrostatic potential field method is introduced,and the characteristics of electrostatic potential field method and artificial potential field method are compared by simulation.In order to solve the problem of low searching efficiency of ant colony algorithm,the cost function of electrostatic potential field method is introduced into the heuristic of ant colony algorithm,which makes the searching ability of the algorithm stronger in complex environment.In the search process of ant colony algorithm,the roll-back mechanism is introduced to reduce the number of dead ants and the invalid paths,which increases the fault tolerance of ants and improves the efficiency of the algorithm.In order to reduce the situation that the local optimal solution in the early stage would lead to the accumulation of pheromones on some edges,which would lead to the premature stagnation of the algorithm,the pheromone volatile factor was made dynamic in this paper to make it self-adaptive and reduce with the increase of iteration times,so as to achieve the purpose of favoring search in the early stage and convergence in the late stage.After the end of each generation of ant exploration,because the pathfinding has a certain randomness,the resulting path will have some sawteeth and windings.These inferior paths will lead to local optimal solutions and slow convergence.This paper proposes a geometric optimization method for each generation path,and then pheromone on the optimized paths is updated,which greatly improving the convergence speed of the algorithm and reducing the path length.Finally,MATLAB software was used for several times of simulation,and the experimental results of the algorithm before and after the improvement were compared to verify the effectiveness and superiority of the optimized algorithm.