Research On Simulation Of Path Planning Of Mobile Robot Based On Improved Cuckoo Search Algorithm

In recent years,with the development of society and the continuous innovation of science and technology,research in the field of robotics is also deepening.As a branch of the robot field,robot path planning has important significance in actual production and application.However,due to the complex and varied operating environment of the robot,many existing intelligent algorithms have their own shortcomings in path planning.Cuckoo Search(CS)is a new group intelligence algorithm proposed in recent years.The design is simple and efficient,the global search ability is strong,and the parameters are less convenient for regulation.To this end,this thesis attempts to solve the robot path planning problem by using the cuckoo search algorithm.Firstly,the understanding of the principle of the cuckoo search algorithm is carried out.Through the study of the cuckoo search algorithm in the related literature,it is found that the traditional cuckoo search algorithm has the disadvantages of slow convergence and easy to fall into local extremum.Therefore,three improvements are proposed for the shortage of the cuckoo search algorithm: the first is to add a dynamic adjustment strategy to the discovery probability,to retain the better quality solution as much as possible,and discard the poor quality solution;the second is to improve the random walk strategy to strengthen the group optimal solution to individual guidance and accelerates the convergence of the algorithm;the third is to join a group leanring learning strategy after random walk strategy to improve the local optimization ability of the algorithm.The improved cuckoo search algorithm(ICS)was tested with the test function set,and compared with the traditional cuckoo search algorithm,particle swarm optimization algorithm,and literature [65] ant colony algorithm to verify the effectiveness of the improved algorithm.The results show that the improved cuckoo search algorithm has Higher precision of optimization.Then,the grid map simulation environment model is established,and the improved cuckoo search algorithm is applied to the robot path planning,and multiple experiments are performed for different size simulation environments.The results show that in the simulation environment of three different scales,the improved cuckoo search algorithm has better optimization effect and higher precision than the other three algorithms.In order to further improve the performance of the improved cuckoo search algorithm,this thesis proposes to fuse the A* algorithm with the improved cuckoo search algorithm,and replace the random initial optimal solution in the cuckoo search algorithm with the better solution generated by the A* algorithm to accelerate the convergence of the algorithm.Speed,and smooth the resulting path,reducing the length of the path and the number of transitions.The simulation comparison experiment of the improved cuckoo algorithm(A-ICS)shows that A-ICS has faster convergence rate than ICS,and the obtained path is shorter and smoother.