关键词: 路径规划, 蚁群算法, 多因子启发式函数, 信息素, 平滑路径

Abstract: Aiming at the problem that current global path planning algorithm for mobile robots with a single solution objective can't cope with the complex and changing environments,a multi-factor improved ant colony algorithm was proposed.The RGB-2D grid method was proposed to simulate the real ground road environment for mobile robots,and the neighborhood matrix exploration method was used to achieve obstacle detection for the diagonal obstacle situation,which effectively improved the safety of the path.To overcome the limitation of traditional path planning with distance as a single indicator,a multi-factor heuristic function that integrated path safety,bumpiness,smoothness and route shortest were constructed.Considering the blindness of the early search of the traditional ant colony algorithm,the principle of initial pheromone step allocation was proposed.The pheromone was classified,and the pheromone on each path were superimposed according to the optimization goal.The maximum and minimum ant strategy and the pheromone volatilisation factor self-adjustment strategy were used to avoid local optimization.The dynamic cut-point adjustment method was applied to smooth the path and further improve the route quality.Simulation experiments showed that the improved algorithm had good adaptability in complex environments and the integrated path performance index was better than the algorithms in comparative literature,which could provide an effective reference for multi-factor path planning in practical environments.

Key words: path planning, ant colony algorithm, multi-factor heuristic function, pheromone, smooth path