关键词: 蚁群算法, 路径规划, 状态转移概率, 信息素更新策略

Abstract: To address the issues of long optimal paths and slow convergence speed in traditional ant colony algorithm for path planning applications,a mobile robot path planning method based on the sparrow search-ant colony algorithm is proposed.In the sparrow search-ant colony algorithm,the sparrow search algorithm is first used to generate a suboptimal path which is then used to establish the initial pheromone distribution for the ant colony algorithm. Next, in the calculation of state transition probability, the heuristic function of the A^* algorithm,a bending constraint factor,and a distance weight coefficient are introduced to make the state transition probability selected by the ant colony algorithm optimal at each node,thereby shortening the optimal path. Finally,in the pheromone update strategy,an adaptive pheromone update strategy based on iteration,angle factors,and a reward-punishment mechanism is employed to accelerate the convergence speed of the ant colony algorithm. According to the simulation results,the suggested method could produce high-quality optimal solutions while simultaneously decreasing the number of turns and increasing convergence speed when compared to alternative approaches. The suggested method has further confirmed its stability and practicability in complex contexts by demonstrating notable improvements in path smoothness and robustness to adapt to complex maps.

Key words: ant colony algorithm, path planning, state transition probability, pheromone update strategy