Robot Path Planning Based On Improved Ant Colony Algorithm

In recent years,with the development of artificial intelligence technology,robot technology has been applied to many fields of human activities.How to further improve the intelligence of the robot in the moving direction has become a research hotspot.To realize the robot's intelligent navigation technology,the first thing to do is to solve the problems related to robot path planning.Although scholars have proposed some algorithms to solve this problem,the most effective algorithm has not yet been found.Therefore,this paper proposes a new and improved ant colony algorithm method to further study the path planning algorithm.In order to overcome the shortcomings of ant colony algorithm in two-dimensional path planning of robots,the following improvement measures are carried out :(1)The non-uniform distribution of the initial pheromone elliptical "two polarization " is proposed,which guides the ant colony in a general direction and avoids blind search in the early stage of the ant colony.(2)An adaptive pheromone volatilization coefficient is proposed to make the ant colony adjust the pheromone itself and enhance the globality of the algorithm.(3)The improvement of heuristic function is proposed.With the process of ant searching for the path,the heuristic function is changed accordingly to reduce the possibility of ant colony falling into local optimum.Finally,quadratic path smoothing is performed on the optimal path obtained by ant colony search.Spline curve is used to delete redundant nodes and smooth the path to reduce the number of turns of the robot,thereby reducing the energy consumption of the robot.Through simulation and comparative analysis,the improved ant colony algorithm has improved the optimization ability and convergence speed of the algorithm in the application of twodimensional path planning.Aiming at the robot path planning in three-dimensional space,a fusion of ant colony algorithm and beetle antennae search algorithm is proposed.Make full use of the strong global search ability of the ant colony algorithm and the strong individual optimization ability of the beetle algorithm.First,use the ant colony algorithm to search for a global path in the threedimensional space,and then let the longicorn perform a second search around the path.Finally,the simulation results show that the improved algorithm can search for a better path and the optimization effect is better.