Research on Path Planning Based on the Fusion Algorithm of Adaptive Ant Colony Optimization and Artificial Potential Field Method

It is a hot topic in the field of road landscape planning technology that a mobile robot can quickly and safely find an optimal path in a multi-obstacle environment. In path planning, in light of the problems of poor cooperation and slow convergence of ant colony algorithm in a known environment, the existing potential field method in the local path environment focuses on avoiding dynamic obstacles but cannot guarantee an optimal path. This study provides a new fusion algorithm for path planning optimization in both static and dynamic environments. Firstly, to prevent slipping into a local optimum, create a pheromone diffusion model and adaptively tweak the population information entropy factor to speed up the convergence speed of the Adaptive Ant Colony Optimization (AACO) algorithm. Secondly, on the basis of the globally planned path, by designing the local stability detection and escape functions, the Improved Artificial Potential Field (IAPF) method is utilized to solve the problem of unreachable destination. Finally, we conduct simulation experiments through MATLAB to compare the indicators for evaluating paths, it verifies that the fusion algorithm proposed in this research has obvious advantages in path planning in both static and dynamic environments.