Research On Robot Path Planning Based On Improved PSO-ACO Algorithm

Intelligent robot movement is an important part of robot research.Path planning technology has become an important research field in the research of mobile robots.The key task of path planning is to search for the smooth non-collision shortest path that can make the mobile robot reach the target point smoothly from the starting point in the uncertain or uncertain obstacle space according to certain evaluation criteria.In this paper,after determining the obstacle space,the shortcomings of particle swarm optimization algorithm and ant colony optimization algorithm are improved,and the two algorithms are effectively fused,and the optimal path is calculated by using the fusion algorithm.The main work of this paper is as follows:Firstly,particle swarm optimization(PSO)is improved for its disadvantage of being easily trapped in the local optimum.The particle traction mechanism is introduced to improve the particle's ability to get out of the trap in the search area with poor fitness and accelerate the convergence speed of the algorithm.By adjusting the global search ability and local search ability of the algorithm by nonlinear dynamic adjustment of inertia weight and learning factor balance,the algorithm can still maintain a good search accuracy under the condition of accelerating convergence speed.Secondly,the PSO algorithm is used to obtain an optimal path in the first round of coarse search,which is transformed into the initial pheromone distribution of ant colony algorithm.Then,the second round of precise search is carried out with the advantages of high search accuracy and positive feedback of ant colony algorithm,and the global optimal path is planned.Again in the MATLAB simulation experiment,through the grid method to establish environment map of three groups of different proportion of obstacles,respectively using the PSO algorithm,ant colony algorithm and fusion algorithm for path planning,by comparing the obtained by the algorithm of shortest path search time,average path length and indicators such as comparative analysis of algorithm performance,it is concluded that the improved PSO algorithm and ant colony algorithm are more primitive algorithm improve the search performance,fusion algorithm and absorbed the advantages of the two kinds of improved algorithm,a fast search speed and high precision,and can be very good to adapt to the complex situation of environment,has a good performance in different environment.Finally,the experimental results show that the fusion algorithm is effective in the complex map environment.The fusion algorithm overcomes the disadvantages of being easily trapped,so as to solve the robot path planning problem in the complex environment map more effectively.