Research on Path Planning of Improved Ant Colony Optimization in Multi-factor Adaptive Grid Model
As a common modeling method in the field of path planning,the grid method has certain requirements for the mod-eling environment and the number of grids.The traditional grid method cannot balance the contradiction between the number of grids and the accuracy of the environment,and cannot restore the complex terrain in the real environment.In order to solve the above problems,this paper firstly proposes a new multi-factor adaptive grid modeling method,which uses grids of different sizes to model complex environments and retains the driving complexity of different terrains to calculate the energy consumption of robots.Then,the article uses the improved ant colony optimization to find the optimal path on the adaptive grid modeling map with the path length and the overall energy consumption of the robot as the goal.The improved ant colony optimization uses A*algorithm and artificial po-tential field to optimize the convergence speed.It makes the path smooth through path optimization.The experimental results show that the path planning of the improved ant colony optimization on the new multi-factor adaptive grid modeling map greatly reduces the number of grids,ensures a high degree of restoration of environmental information,and greatly improves the convergence speed and convergence accuracy of path planning.Moreover,the algorithm can be applied to various terrain environments.
万方数据
维普
