Improved Ant Colony Algorithm And Its Application In Path Planning

With the rapid development of science and technology,the requirements for path planning technology in various fields are constantly improving.However,there are some limitations in the application of traditional intelligent algorithm in path planning technology.Therefore,improving the performance of traditional intelligent algorithm and applying it to path planning technology become a research focus.As a common intelligent algorithm,ant colony algorithm not only has the characteristics of parallelism,self-organization,positive feedback,and robustness,but also has good optimization ability.Therefore,it is widely used in solving practical problems such as data clustering analysis,path planning and vehicle scheduling.However,the traditional ant colony algorithm also has some obvious shortcomings,such as easy to fall into local optimization and low search efficiency.Therefore,it is of great significance to improve the shortcomings of traditional ant colony algorithm and apply it to path planning problem.In this thesis,the traditional ant colony optimization algorithm is easy to fall into the local optimal and low search efficiency is improved.The improved ant colony algorithm is applied to two-dimensional and three-dimensional environments,and the effectiveness of the improved ant colony algorithm in two-dimensional and three-dimensional environments is verified by algorithm simulation.The main work arrangements are as follows:1.The research status of path planning and common path planning algorithms are introduced in detail.The development status of ant colony algorithm is introduced in detail.2.The basic principle of ant colony algorithm and several classic improved ant colony algorithms are introduced in detail,and the influence of various parameter settings on the performance of the algorithm is discussed and analyzed.In addition,the characteristics of ant colony algorithm are introduced in detail.3.In the two-dimensional path planning,aiming at the problems that the ant colony algorithm is easy to fall into local optimization and low search efficiency,the state transition probability and pheromone update strategy in the original ant colony algorithm are improved.Aiming at the problem that ant colony algorithm is easy to fallinto deadlock in two-dimensional path planning,a conditional back-off ant colony algorithm is introduced.In order to further improve the quality of the search path,the improved ant colony algorithm and frog leaping algorithm are mixed.Firstly,the improved ant colony algorithm is used to obtain the initial path,and then the path generated by the ant colony algorithm is further optimized by using the improved frog leaping algorithm.Finally,a simplified operator is used to optimize the generated path to avoid excessive corners in the path and to ensure the smoothness of the path.Experimental results verify the effectiveness of the improved ant colony algorithm in two-dimensional path planning.4.Aiming at the problem of slow convergence speed and easy to fall into local optimization in three-dimensional path planning of the original ant colony algorithm,the heuristic function of the ant colony algorithm and the search mode in 3d environment are improved.The specific improvements include the following four aspects: Firstly,while ensuring the overall search capability of the algorithm,the pseudo-random state transfer strategy is applied to the selection of feasible nodes in order to improve the algorithm's trend capability.Secondly,the concepts of feasible factor,distance factor,and safety value function are introduced into the heuristic function.While ensuring the safety of the path,the convergence speed of the algorithm is accelerated.Thirdly,aiming at the problem that the low concentration of path pheromone in the initial stage and the high concentration of path pheromone in the later stage lead to the slow searching speed of the algorithm in the early stage and the insufficient global searching ability in the later stage,dynamic local pheromone update and dynamic global pheromone update are combined.Finally,according to the ant colony algorithm's search process in the three-dimensional environment,a path search is performed by combining the plane-by-plane search and the field of view.Experimental results verify the effectiveness of the improved ant colony algorithm in three-dimensional path planning.