Research On Path Planning Of Mobile Robot Based On Improved Ant Colony Algorithm

In this paper,the ant colony algorithm is improved for the path planning of mobile robots.The fast convergence is realized in the global path planning.The path lengths found are shorter and the paths are smoother.Mainly include the following:(1)The information of the robot's working environment is known to improve the convergence speed of the algorithm by improving the initial pheromone concentration distribution of the ant colony algorithm to induce the ant colony to search from the starting point to the end point.(2)Improve the algorithm by mixing the ant colony algorithm and the genetic algorithm to realize the improvement of the performance of the ant colony algorithm.In each iterative optimization process of the ant colony algorithm,the immune operator is introduced,that is,the ant colony search.In the process,the next node is selected according to the node selection probability,until all ants reach the end point,all the paths formed at this time are called the initial population,and then after selection,the excellent individuals are selected and then crossover and mutation are performed.Some genes on the individual are changed to form new individuals to improve the quality of the solution.Through such an improved approach,the global optimization ability of the ant colony algorithm and the ability to search for continuous space problems are enhanced.(3)In the improved algorithm,the pheromone updating method of the ant colony algorithm is also optimized.Finally,a nonlinear optimization is added to enhance the local search capability.(4)Finally,the effectiveness of the improved algorithm is verified by simulation experiments,and the performance of the improved algorithm is better than that of ant colony algorithm and adaptive ant colony algorithm.